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CEO Interview: Ganesh Verma, Founder and Director of MoogleLabs

CEO Interview: Ganesh Verma, Founder and Director of MoogleLabs
by Daniel Nenni on 03-22-2024 at 6:00 am

MicrosoftTeams image (27)

A thought leader with demonstrated history in multiple roles- project life cycles, ideation, implementation, and closing projects delivering business value and delighting stakeholders, crafting technical aspects of the company’s strategy for aligning with the business goals perfectly, discovering and implementing technologies to yield competitive advantage in the digital landscape.

Being a Six Sigma Green Belt holder, I hold a wide exposure and experience of working in an agile environment, managing and executing scrums and enabling teams to set new milestones within a minimum timeframe while delivering superior outputs on the customer’s end. Besides that, I am a result-oriented and business-focused with hands on experience on broad range of next-gen technologies.

 Tell us about your company. 
MoogleLabs is an organization that offers services in all revolutionary technologies including AI, ML, DevOps, Data analytics, metaverse, blockchain, Web3, and several more. When I was working in the IT industry, I saw a major gap between what was being offered as solutions to organizations and where the latest technology was at that point. Limited organizations were working with revolutionary technologies as the adoption rate is slow.

With MoogleLabs, we change that. With primary focus on the latest technology, we offer state-of-the-art services to our clients, creating the most advanced products for them every time. I ideated the startup back in 2020, and since then have not looked back. Now, we have over 100 employees who are working on a range of products and I couldn’t be happier.

What problems are you solving? 
At the core, MoogleLabs is an IT company. So, we are focused on assisting organizations in creating innovative solutions to improve their operations. What we do differently is that we offer services in the tech stack that are not readily available in the market. It also means that we are better equipped to create solutions that will be as per the market standards for years to come. Think of it this way: when you buy a mobile, if you choose to purchase an older model because it is still relevant, you save money. However, you compromise on the longevity of the product. The updates will stop coming soon, and in only a year or two, you will not have a relevant model. However, when you opt for the latest model, it will get the updates and stay relevant longer. We do the same but for software. We create solutions that will not turn obsolete in a matter of a few years if not months.

What application areas are your strongest?
The three technologies that we have been working on constantly are AI, ML, and Blockchain. Artificial Intelligence and machine learning are two technologies that will revolutionize world operations as we know them. They can automate a range of tasks for businesses, making them more efficient. Also, blockchain can make the internet more secure and safer for users.

We are currently working on creating AI-enabled IoT that will assist with business operations. Additionally, we are working on blockchains, crypto bots, metaverse, and much more. However, I think I am biased toward our capabilities to offer AI services.

What keeps your customers up at night? 
Our customers are innovators. Always looking for ways to make their business better, ensuring that it stands out among the competitors and offers something different. So, I guess ways to bring innovation to their business are what keeps them awake at night.

Once they start working with us to create the appropriate solution, the worries go away.

What does the competitive landscape look like, and how do you differentiate?
The number of IT organizations in the world is increasing as we speak. So, it is a seriously competitive market. We differentiate from others because we are dedicated to working with the latest technologies. For more IT organizations, the biggest pitfall is not evolving the latest technologies. For older companies, it is easier to fall back on what they know and not pay attention to what new has entered the market, ultimately losing in the race. As a startup, we are current in the tech stack. Additionally, with the focus being on revolutionary technologies at all times, the trend will continue. This is what helps set us apart from others and get so many results.

What new features/technology are you working on? 
Well, there are a few already completed projects that I am currently proud of. For one, we have created an application that extracts information from uploaded documents to get insights for better decision making. It is called Alluvium. Other projects we have worked on include screen damage detection, fully automated assessment solutions, and more. We have also worked on agile DevOps projects, NFTs, blockchain wallets, and many others. We are working on several other concepts in this section and aim to provide the more innovative solutions in the future.

 How do customers normally engage with your company?
Companies that want to connect for work can get in touch through our MoogleLabs’ Contact Us page or can email us at info@mooglelabs.com Then, our team gets in touch with them to discuss the work details.

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Semiconductor CapEx Down in 2024

Semiconductor CapEx Down in 2024
by Bill Jewell on 03-21-2024 at 2:00 pm

Delayed Wafer Projects

U.S. President Biden announced on Wednesday an agreement to provide Intel with $8.5 billion in direct funding and $11 billion in loans under the CHIPS and Science Act. Intel will use the funding for wafer fabs in Arizona, Ohio, New Mexico, and Oregon. As reported in our December 2023 newsletter, the CHIPS Act provides a total of $52.7 billion for the U.S. semiconductor industry, including $39 billion in manufacturing incentives. Prior to the Intel grant, the CHIPS Act had announced grants totaling $1.7 billion to GlobalFoundries, Microchip Technology, and BAE Systems, according to the Semiconductor Industry Association (SIA).

Grants under the CHIPS Act have been slow in coming, with the first grants announced over a year after passage. Some major fab projects in the U.S. have been delayed due the slow disbursement. TSMC also cited difficulties in finding qualified construction personnel. Intel said the delay was also due to slowing sales.

Other nations have also allocated funds to promote semiconductor production. The European Union in September 2023 passed the European Chips Act which provides for 43 billion euro (US$47 billion) of public and private investment in the semiconductor industry. In November 2023, Japan allocated 2 trillion yen (US$13 billion) for semiconductor manufacturing. Taiwan in January 2024 enacted a law to provide tax breaks for semiconductor companies. South Korea in March 2023 passed a bill to provide tax breaks to strategic technologies including semiconductors. China is expected to create a $40 billion fund backed by the government to subsidize its semiconductor industry.

What is the outlook for capital expenditures (CapEx) in the semiconductor industry this year? The CHIPS Act was designed to spur CapEx, but much of the effect will not occur until after 2024. After a disappointing 8.2% decline in the semiconductor market last year, many companies are cautious about CapEx in 2024. We at Semiconductor Intelligence estimate total semiconductor CapEx in 2023 was $169 billion, down 7% from 2022. Our forecast is a 2% decline in CapEx in 2024.

The major memory companies are generally increasing CapEx in 2024 as the memory market recovers and new applications such as AI are expected to increase demand. Samsung plans relatively flat spending in 2024 at $37 billion but did not cut CapEx in 2023. Micron Technology and SK Hynix cut back CapEx significantly in 2023 and are planning double-digit increases in 2024.

The largest foundry, TSMC, plans to spend about $28 billion to $32 billion in 2024, with the midrange of $30 billion down 6% from 2023. SMIC is planning flat CapEx while UMC plans a 10% increase. GlobalFoundries expects a 61% cut in 2024 CapEx but will ramp up spending in the next few years as it builds a new fab in Malta, New York.

Among integrated device manufacturers (IDMs), Intel plans to increase CapEx in 2024 by 2% to $26.2 billion. Intel will increase capacity for foundry customers as well as for internal products. Texas Instruments’ CapEx is roughly flat. TI plans to spend about $5 billion a year through 2026, primarily for its new fabs in Sherman, Texas. STMicroelectronics will cut CapEx 39% while Infineon Technologies will cut by 3%.

The three largest spenders – Samsung, TSMC and Intel – will account for 57% of semiconductor industry CapEx in 2024.

What is the appropriate level of CapEx relative to the semiconductor market? The semiconductor market is notoriously volatile. Over the last 40 years, annual change has ranged from 46% growth in 1984 to a 32% decline in 2001. Although the industry has become somewhat less volatile as it has matured, in the last five years it has shown a 26% increase in 2021 and a 12% decrease in 2019. Semiconductor companies need to plan their capacity several years out. It takes about two years to build a new wafer fab and additional time for planning and financing. As a result, the ratio of semiconductor CapEx to the semiconductor market varies greatly, as shown below.

The semiconductor CapEx to market size ratio has varied from a high of 34% to a low of 12%. The five-year average ratio ranges between 28% and 18%. Over the total period of 1980 to 2023, the total CapEx was 23% of the semiconductor market. Despite the volatility, the long-term trend of the ratio has been fairly consistent. Based on expected strong market growth and a drop in CapEx, we expect the ratio to drop from 32% in 2023 to 27% in 2024.

Most forecasts for semiconductor market growth in 2024 are in the range of 13% to 20%. Our Semiconductor Intelligence forecast is 18%. If 2024 turns out to be as strong as expected, companies will likely increase their CapEx plans as the year progresses. We could then see positive change in semiconductor CapEx in 2024.

Semiconductor Intelligence is a consulting firm providing market analysis, market insights and company analysis for anyone involved in the semiconductor industry – manufacturers, designers, foundries, suppliers, users or investors. Please contact me if you would like further information.

Bill Jewell
Semiconductor Intelligence, LLC
billjewell@sc-iq.com

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2024 Outlook with John Lee, VP and GM Electronics, Semiconductor and Optics Business Unit at Ansys

2024 Outlook with John Lee, VP and GM Electronics, Semiconductor and Optics Business Unit at Ansys
by Daniel Nenni on 03-21-2024 at 10:00 am

John Lee Headshot

We have been working with Ansys since SemiWiki was founded in 2011. It has been a richly rewarding relationship in all regards. I always say the semiconductor industry is filled with the most intelligent people in the world and Ansys is an excellent proof point. I have known John Lee for 30+ years and he is one of my trusted few, absolutely.

Tell us a little bit about yourself and your company.
I have been an EDA person for most of my career. I was really blessed to be a graduate student of Professor Ron Rohrer at Carnegie Mellon University, in a research group that launched the careers of many highly successful students, including three CEOs and two Kaufman Award winners.

I’m also blessed to be part of Ansys. We have over 6,000 employees worldwide and our products power innovation that drives human advancement across a very broad array of industries and technical challenges. For example, we have an ongoing partnership with Oracle Red Bull Racing to help them remain the most competitive Formula 1 (F1) team in the world. Oracle Red Bull Racing used Ansys Fluent for streamlining simulations, Ansys Granta for optimizing material choices, and Ansys LS-DYNA to verify safety parameters. They were able to design and optimize a race car that driver Max Verstappen used to make history in 2023, winning 21 out of 23 races and secure the Drivers’ and Constructors’ Championships with a massive 413-point margin.

Another example is how NASA engineers used capabilities from Ansys AGI to steer the James Webb Telescope by simulating the complex gravitational perturbations acting on its orbit and estimate station-keeping requirements. The project also relies on Ansys Zemax optical simulation to align the segmented mirror optics and Ansys Mechanical to model the natural vibration modes of the mirror as it is being pointed.

I hope these examples give you an idea of the amazingly broad range of physics simulation solutions supported by Ansys, which include Thermal, Mechanical, Semiconductors, Electromagnetics, Optics, Photonics, Fluid Dynamics, Acoustics, 3D Design, Materials, Safety, Digital Twins, Autonomous Vehicles, Embedded Software, and Mission Engineering.

My responsibility at Ansys is for the Electronics, Semiconductors and Optics products. Our focus has been very rewarding, as the need for open and extensible Multiphysics platforms and solutions has become very clear over the last five years. We’ve been fortunate to establish ourselves as both thought leaders and trusted partners with market-shaping customers.

What was the most exciting high point of 2023 for your company?
The rapid adoption of 2.5D and 3D heterogeneous designs has been both very challenging and very rewarding. Many of these systems are being driven by the AI revolution. These systems consume massive amounts of power, which naturally generates tremendous thermal effects, which induce mechanical effects such as stress and strain that affect both performance and reliability.

The thermal-centric challenges of 3D-IC are even more profound, and if you look at the new systems being designed by the leaders in AI, you can see why 2023 was very exciting and rewarding for Ansys.

A beneficiary of this work is that the AI methods we’ve developed at Ansys are further accelerated. For example, we combined AI with our revolutionary sigmaDVD technology in RedHawk-SC, to enable significantly better PPA from place and route tools. Our leading customers deployed this in 2023, and we see accelerated adoption in 2024. As power and thermals strongly affect advanced process node design, using AI + sigmaDVD has become a very, very exciting high point!

What was the biggest challenge your company faced in 2023?
Ansys has grown significantly during the pandemic, both organically and inorganically.  Since 2020, for example, we’ve announced 10 acquisitions, and we’ve grown rapidly in new offices, including Athens, Vancouver, and Rwanda!

So, figuring out how to onboard many new hires across many regions and also integrate great high-performing teams from our acquisitions has been a challenge and focus of mine in 2023.

How is your company’s work addressing this biggest challenge?
Culture and engagement have been a real focus on the leadership team at Ansys before, and especially during the pandemic. It has been very rewarding to see the focus on this challenge pay off. For example, we are proud that the Wall Street Journal named Ansys one of the 250 best-managed companies of 2023, and Newsweek recognized Ansys as one of the top 100 Most Loved Workplaces.

What do you think the biggest growth area for 2024 will be, and why?
The insatiable and pervasive demand for compute is driving the convergence between silicon and systems, which is creating great opportunities for companies like Ansys.

First, there is an accelerated need for open and extensible Multiphysics platforms – such as Ansys AEDT and SeaScape (for system and chip designers) to partner with leading design platforms – and our investments in the areas of physics, platform and partnerships is driving deep customer value.

Second, both semiconductor and system companies are focused on the benefits of optimized software + silicon systems. This is occurring in automotive, with software defined vehicles; in communications, with 5G and 6G systems; and in the data center, with power and thermal limited compute. Concepts which are missing from the EDA lexicon – such as MBSE, SPDM, SIL and digital twins – are a big opportunity for companies like Ansys.

How is your company’s work addressing this growth?
The Ansys team and portfolio is a great combination of core physics, advanced computational sciences (including AI, cloud and platform), and system-level products that deliver on the need for model-based systems engineering, functional and cyber-security, powerful digital twin models between component and system designers. You’ll see many exciting announcements in this area this year!

What conferences did you attend in 2023 and how was the traffic?
We’ve been excited by the interest in co-packaged optics (CPO), which is a key trend for anyone designing products for the data center and AI. So, Photonics West and Optical Fiber Conference (OFC) were great.

DesignCon is another event that has really rebounded since the pandemic, and the convergence between silicon and system is driving lots of interest in Ansys Multiphysics.

Will you attend conferences in 2024? Same or more?
Mobile World Congress is one that we just came back from, and it’s been exciting to see how Ansys technologies are helping 5G communication systems deploy using our Ansys RF Channel Modeler and AGI Systems Toolkit (STK). For classic EDA folks like me, it’s very exciting to see how computational physics and missions planning are playing a vital role in connecting the world.

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Simulating the Whole Car with Multi-Domain Simulation

Simulating the Whole Car with Multi-Domain Simulation
by Bernard Murphy on 03-21-2024 at 6:00 am

Simulating the Whole Car with Multi-Domain Simulation

Next significant automotive blog in a string I will be posting (see here for the previous blog).

In the semiconductor world, mixed simulation means mixing logic sim, circuit sim, virtual sim (for software running on the hardware we are designing) along with emulation and FPGA prototyping. While that span may seem all-encompassing, in fact, it’s still a provincial view. OEMs like auto companies develop complete products in which software plays an outsize role, governing what in effect is a highly distributed compute system across the car. Developing and testing this software on (software-based) digital twins allows for faster experimentation and high levels of parallelism than possible with hardware prototypes but requires collaboration between many kinds of domain specific simulators. A very diverse group of companies are planning to launch a working group under Accellera to define an enabling standard to serve this “Federated Simulation” need.

Who Wants Federated Simulation and Why?

At DVCon I met with an impressive group: Mark Burton (Vice-Chair of the proposed working group, also Qualcomm), Yury Bayda (Principal Software Engineer at Ford Motor Company, previously Intel), Trevor Wieman (System Level Simulation Technologist at Ford Motor Company, also previously Intel), Lu Dai (Accellera Chair and Qualcomm) and Dennis Brophy (needs no introduction). What follows is primarily a synopsis of inputs from Mark, Yury and Trevor.

A car is a network of interconnected computers developed by multiple suppliers; the auto OEM software team must develop/refine and debug software to make this whole system run correctly. Perhaps the infotainment system is based on a Qualcomm chip, communicating with zonal controllers, in turn talking to edge sensors, drivetrain MCUs and other devices around the car, all communicating through CAN or automotive Ethernet. To meet acceptable software simulation times this total system model must run on abstracted virtual models for each component. Suppliers provide such models in a variety of formats: proprietary instruction set simulators or representations based on different virtual modeling tools.

Which raises the perennial problem of blending all these different models into a unified virtual runtime. Maybe someday all suppliers will provide models with TLM-compliant interfaces but until then, can we build better bridges/wrappers to couple between all these models? That’s what the Federated Simulation initiative aims to address. Yury provided a compelling example for why we need to make this work. Over the Air (OTA) software updates are a must-have for software-defined vehicles but what happens if something goes wrong in an update – if the update bricks your car or some part of your car? System-level scenarios like this must be considered during design to mitigate such problems and must tested exhaustively.

Bottom-line, system software development must start early before hardware is available and is completely dependent on reliable high-level simulation abstractions to underpin total system simulations.

Not just for electronic systems

A car is not just electronic circuits; neither is a plane or a spacecraft or an industrial robot. Still, electronics plays an increasing role, now interacting with mechanical systems and with the surrounding environment. Antilock braking must behave appropriately under different levels of traction on a dry road, in rain or in snow. From ADAS to autonomy, driving systems must be tested against a vast array of scenarios. The CARLA simulator is an important component of such testing, modeling urban and other layouts across many environment conditions and providing streaming video, LIDAR, and other sensor data as input to full system simulations.

A federated simulation solution must couple to simulators like CARLA. Ultimately it must also couple with standards in other verticals such as OpenCRG to describe road surfaces, VISTAS/VHTNG for avionics, SMP2 for space applications, and FMUs for mechatronics. Each is well established in its own domain and unlikely to be displaced. A federated simulation standard must respect and smoothly interoperate with these standards – I’m guessing in incremental steps. That said there is already enthusiastic support from many quarters to be involved in this effort.

 

Accellera

Agnisys, Inc.
Airbus
AMD
Aptiv
Cadence Design Systems, Inc.
Collins
Doulos Ltd.
Ford
Huawei Technologies Sweden AB
IEEE

Intel Corporation

IRT-Saint Exupery
Marvell International Ltd
Microsoft Corporation
MachineWare GmbH
NXP Semiconductors
Qualcomm Technologies, Inc.

Robert Bosch GmbH
Renesas Electronics Corp.
S2C
Shokubai
Siemens EDA

Spacebel
STMicroelectronics
Synopsys
Shanghai UniVista Industrial Software GroupTexas Instruments
Vayavya Labs
Zettascale

Core team membership for the initial definition

 What will it take?

This is an ambitious goal but it’s worth noting that the US DoD launched a similar effort called HLA in the 1990s which has continued to grow. Airbus has built their own architecture with similar intent, including a physical prototype of an aircraft for hardware in the loop testing. At the electronic systems level, Mark, Yury and Trevor have all previously been involved in multi-simulator projects at Intel and Qualcomm, and more recently with Ford (Yury and Trevor). They do not see this as an impossible goal though I’m guessing it will likely evolve from modest expectations through multiple releases.

The core concept as described to me is based on cloud deployment with a container instance for each simulation and Kubernetes for resource allocation (CPUs, GPUs, hardware accelerators, etc.) and orchestration. The Accellera team don’t plan to reinvent any standards (or emerging standards) that already work well. Instead they intend to leverage existing transport layers, adding only applications layers above that level such that a simulator instance can publish streams of activity to other subscribing simulators, and subscribers can be selective about what data they want to see.

Very interesting. You can learn more HERE and HERE

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QuantumPro unifies superconducting qubit design workflow

QuantumPro unifies superconducting qubit design workflow
by Don Dingee on 03-20-2024 at 10:00 am

Superconducting qubit design workflow in QuantumPro

To create quantum computing chips today, a typical designer must cobble various tools together, switching back and forth between them for different tasks. By contrast, EDA solutions such as Keysight Advanced Design System (ADS) unify a design workflow in a single interface with automated data exchange between features. In an industry-first, Keysight QuantumPro brings five different functions for quantum design together in a superconducting qubit design workflow, reducing design cycle time and prototyping and yield risk for optimized quantum chips. Keysight’s Quantum EDA solution focuses on accurate electromagnetic modeling, ensuring that simulation and measurement outcomes align effectively.

Adding superconducting qubits presents a yield challenge

Superconducting quantum computers rely on qubits constructed from Josephson junctions held at cryogenic temperatures, which exhibit non-linear inductance needed in constructing two-level systems. Qubits interconnect by meander line coplanar waveguide resonators with frequencies often in the 4 to 10 GHz range. The resonators serve two primary functions: indirectly reading out the state of the qubits and entangling them with each other. Quantum amplifiers with gain and unique ultra-low noise characteristics amplify the output signal from qubits to improve the readout fidelity. Quantum processing units (QPUs), comprising arrays of qubits and resonators, are positioned to showcase quantum advantage, surpassing the reach of classical CPUs.

The advantage of quantum computing arises from its distinct exponential increase in computational capacity as the number of entangled qubits grows, with n qubits taking on 2n stable states. Scaling the number of qubits in quantum computing presents a formidable challenge, with the stability and coherence of qubits becoming more complex with each addition. By tying four design themes – structural layout, electromagnetic analysis, complex quantum circuit optimization, and system-level exploration and troubleshooting – into a single quantum design workflow, designers can thoroughly exercise every part of a design, make data-based adjustments, and re-simulate to verify improvements.

Unlike digital circuit design, the challenge of designing quantum chips is more than step and replication. For entanglement to work correctly, resonance frequencies must be unique within and between all nearby qubits. If two (or more) resonance frequencies overlap, qubits entangle improperly due to unpredictable cross-coupling, and the quantum chip becomes a yield failure. Factors such as minimizing environmental interference, maintaining qubit entanglement, and managing errors due to decoherence pose significant obstacles. Debugging problems discovered after fabricating chips and cryogenic testing become expensive and time-consuming.

Enabling a five-point superconducting qubit design workflow

A better solution is a shift left for quantum chip design – integrating layout and simulation tools to predict and optimize resonance frequencies accurately in virtual space. RF designers are familiar with these workflows, but quantum designers are just beginning their adoption. “QuantumPro bridges the gap from ad-hoc quantum chip design with inherent yield risks to confidence in layout and simulation for predictable parts,” says Mohamed Hassan, Quantum Solutions Planning Lead at Keysight.

QuantumPro integrates five functions built on the ADS platform to streamline superconducting qubit designs, including schematic design, layout creation, electromagnetic (EM) analysis, non-linear circuit simulation, and quantum parameter extraction. Beginning with the schematic interface, users can effortlessly drag and drop components from the built-in quantum artwork. Subsequently, a layout can be generated automatically from the schematic.

Within QuantumPro, two distinct analyses are available. First, the Full EM Analysis facilitates a frequency sweep of circuits, producing s-parameters at input and output ports. The platform supports both the finite element method (FEM) and the method of moments (MoM) for the Full EM analysis. Second, the Energy Participation Analysis allows for finding the eigenmodes of the system with the FEM solver. Instead of solving for the electric field over the entire volume, the MoM solves only for the currents on the metal surface, significantly cutting computational costs.

Quantum parameter extraction is automatic in QuantumPro, with quasi-static, black box quantization, and energy participation ratio (EPR) methods. A simplified layout of a four-qubit design shows the transmon qubits (Q1 through Q4) and meander line resonators (R1 through R4). Note the unique resonance frequency values extracted by each of the three different methods in QuantumPro.

EM simulation of superconducting qubits requires one extra step. Superconductors exhibit kinetic inductance, an additional inductance large enough to sway results compared with perfect electric conductors. “Designers can’t ignore kinetic inductance – it can cause a miss in resonance frequency by as much as 40% in some cases of thin film superconductors,” says Hassan. Superconductor material editors in ADS and EMPro allow designers to describe materials for accurate kinetic inductance capture.

A core feature from ADS in QuantumPro is a Python console that gives users more control over workspaces and user interfaces and the ability to script and automate repetitive tasks.

Scaling quantum chips to hundreds or thousands of qubits

QuantumPro assures superconducting quantum chip designers get the results they expect at prototyping after simulation and optimization of designs. QuantumPro arrives at a pivotal point in quantum computing development as designers seek to move from designs with tens of qubits into the hundreds and perhaps thousands. As in conventional semiconductors, reducing prototype re-spins and improving yields can help move ideas from research to commercialization more quickly.

Designers will also see a productivity boost in their superconducting qubit design workflow with QuantumPro, which may also lower the learning curve for designers from other disciplines. Resources on the Keysight website explain more about the science behind superconducting qubits and the EM analysis and parameter extraction methods in QuantumPro.

QuantumPro webpages, with links to an application note, technical overview, and videos:

Quantum EDA: Faster design cycles of superconducting qubits

W3037E PathWave QuantumPro


sureCore Enables AI with Ultra-Low Power Memory IP

sureCore Enables AI with Ultra-Low Power Memory IP
by Mike Gianfagna on 03-20-2024 at 8:00 am

sureCore Enables AI with Ultra Low Power Memory IP

We all know that AI is becoming pervasive in a wide array of products to make them smarter, safer and feature rich. Just look at the announcements from the recent CES show in Las Vegas to see some examples. These AI workloads demand a lot of compute power. Fueling this trend is the need for significant arrays of embedded memory on chip, as close to the compute units as possible. This is a way to reduce latency, but it also brings a lot of power challenges and packaging/cooling headaches. sureCore is well-known for its ultra-low power memory solutions and the company recently announced a significant addition to its arsenal to address the vexing embedded memory problem for AI systems. Let’s see how sureCore enables AI with ultra-low power memory IP.

The New sureCore Solution

sureCore is no stranger to exotic and cutting-edge applications. You can learn about what the company is doing to enable quantum computing here. The new offering from sureCore is called PowerMiser AI. The inferencing power required by new applications needs massively parallel processing arrays. These technologies increase power consumption and thermal load, making packaging and cooling more challenging.

SureCore modified its PowerMiser IP by optimizing it to reduce dynamic power and  leveraging power efficiencies offered by FinFET technology. The result is PowerMiser AI, a new product that reduces thermal impact and addresses the performance demanded by AI workloads.

Digging a bit deeper, embedded SRAM can cause substantial issues for workloads such as pattern matching. As a result, memory can contribute as much as 50% of the power on a large AI-enabled chip. sureCore estimates that using PowerMiser AI can reduce dynamic power by up to 50%. This reduces thermal load, so heat sinks or other cooling approaches are either not needed or significantly reduced. The result is increased system reliability and lower cost. 

To Learn More

Beyond memory IP, sureCore offers a custom memory development service called sureFIT.  Using this service, designers can get memory subsystems optimized for the target power, performance and area (PPA) of the project. In addition to custom solutions, sureCore has a range of power-optimized standard products that deliver market-leading power profiles. These include Everon, PowerMiser, and MiniMiser. Further details can be found at on sureCore’s product page.

Power savings are available at both the nominal operating voltage and at low to near threshold voltages, providing many choices. sureCore memories offer single rail low voltage operation that allows direct logic connection, which eases system-level design.

There was a recent press release announcing PowerMiser AI. You can read the full release here.

In that release, Paul Wells, sureCore’s CEO went into some detail about what drove the development of the new product:

“Our typical customer has been using our ultra-low power SRAM IP in battery-powered applications to provide a longer operational life between recharges. The surge in AI augmentation means that whole new areas for our low power memory solutions have appeared in new and exciting areas that are not constrained by battery life and can be mains powered or are even in the automotive space.

Power consumption is still a critical factor for these applications, but the constraining factor is starting to become heat dissipation and potential thermal damage. In order to keep product form factors under control and obviate the need for forced cooling so as to prevent overheating, new low power solutions are needed. Our recent announcements about working on ultra-low power memory IP for use in cryostats in the quantum computing arena, where heat generation by chips has to be minimised, has resulted in enquiries from companies who also need to keep AI chips operating within temperature boundaries albeit at the other end of the scale.”

And that’s how sureCore enables AI with ultra-low power memory IP.

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sureCore Enables AI with Ultra-Low Power Memory IP

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Challenge and Response Automotive Keynote at DVCon

Challenge and Response Automotive Keynote at DVCon
by Bernard Murphy on 03-20-2024 at 6:00 am

dvcon 2024 keynote min

Keynotes commonly provide a one-sided perspective of a domain, either customer-centric or supplier-centric. Kudos therefore to Cadence’s Paul Cunningham for breaking the mold in offering the first half of his keynote to Anthony Hill, a TI fellow, to talk about outstanding challenges he sees in verification for automotive products. Paul followed with his responses, some already in reach with existing technologies, some requiring more stretching to imagine possible solutions, and some maybe out of range of current ideas.

Anthony set the stage with his breakdown of macro trends in automotive systems design. ADAS and autonomy are pushing more automation features like lane assist and driver monitoring, in turn driving more centralized architectures. Connectivity is critical for OTA updates, vehicle to vehicle communication, in-cabin hot spots. And electrification isn’t just about inverters, motor drivers (now charging at higher power levels and frequencies). It’s also about all the electronics around those core functions, for battery monitoring and for squeezing maximum power efficiency to extend range.

These trends drive higher levels of integration to increase capabilities and to reduce latencies for responsiveness and safety. Pulling in more IPs to build more complex systems, increasing distributed mixed-signal operation around PLLs, DDRs etc., and adding more complex tiered network on chip (NoC) bus fabrics. Chiplet requirements now central at some big auto OEMs will add yet more verification complexity.

Anthony’s Challenges

Anthony shared challenges he sees growing in importance through this increased complexity. Integrating IPs from many sources remains challenging. Not so much in basic functionality and timing, more in non-executable (documentation) guidance on usage limitations or limits to the scope of testing for functionality, performance, safety, configuration options, etc. Relying on doc for communicating critical information is a weak link, suggesting more opportunities for standardization.

In functional safety (FuSA), standards for data exchange between suppliers and consumers are essential. FMEDA analysis is only as good as the safety data supplied with each IP. Is that in an executable model or buried somewhere in a document? Can formal play a bigger role in safety than it does today, for example in helping find vulnerabilities in a design during the FMEA stage?

For mixed signal, he’s seeing more cases of digital embedded in analog with corresponding digital challenges like CDC correctness. These are already solved in verification for digital but not for mixed A/D. Monte-Carlo simulations are not adequate for this level of testing. Can we extend digital static verification methods to mixed A/D?

As NoCs become tiered across large systems, non-interlocked (by default), and with significant room for user-defined prioritization schemes, it is become more challenging to prove there is no potential for deadlocks, more generally to ensure compliance with required service level agreements (SLAs). Simulation alone is not enough to ensure, say, that varying orders of command arrival and data return will work correctly under all circumstances. Is a formal methodology possible?

Another very interesting challenge is multicycle glitch detection in non-traditional process corners. Gate level simulation across PVT corners may claim a multicycle logic cone is glitch free but still miss a glitch in a non-standard corner. Anthony speculates that maybe some kind of “formal” verification of logic cones with overlapping timing constraints could be helpful.

Finally, in chiplet-based designs, today he sees mostly internal chiplets plus external memory for HPC-class busses, but over time expects multi-source chiplets will amplify all the above problems and more over current IP-level integration, pushing additional requirements onto what we expect from models and demanding more standardization.

Paul’s Responses

Paul opened by acknowledging that Anthony had a pretty good challenge list since he (Paul) was only able to find slides among his standard decks to address about 50% of the list. For the rest he sketched ideas on what could be possible.

For system integration (and by extension multi-die systems), System VIPs and other system verification content are now extending the familiar IP-level VIP concept up to more complex subsystems. For example, the Cadence Arm SBSA (server-based system architecture) compliance kit provides all the components necessary for that testing. This concept is naturally extensible to other common subsystems, even extending into mixed signal subsystems, adding further value through stress testing across multiple customers and designs. Connectivity checking is another variant on the system-level verification theme, not only for port-to-port connections but also for connection paths through registers and gating.

Verification management solutions provide a structured approach to system verification, from requirements management, spec annotation, and integration with PLM systems. Additionally, verification campaigns today span multiple engines (virtual, formal, simulation, emulation, prototyping) and regression testing through the full evolution of a design. Taking a holistic and big data view together with machine learning enables design teams to understand the whole campaign and how best to maximize throughput, coverage, and debug efficiency. In-house systems with similar purpose exist of course, but we already see enthusiasm to concentrate in-house R&D more on differentiating technologies and to offload these “verification management” functions onto standard platforms.

In safety, Paul sees current verification technologies as a starting point. Fault simulation and support for FMEDA analysis with fault campaign strategies is already available, together with formal reachability analysis to filter out cases where faults can’t be observed/controlled. The EDA and IP industry is tracking the Accellera working group activity on an interoperable FuSA standard and should become actively involved with partners as that work starts to gel. There is also surely opportunity to do more: enabling fault sim in emulation will amplify capacity and performance and will allow us to quantify impact of safety mechanisms on performance and power. Paul hinted that more can also be done with formal as Anthony suggests.

Verifying mixed signal hierarchies is an interesting challenge. Analog embedded in digital, digital embedded in analog, even inline bits of digital in analog. DV-like verification in these structures will be a journey, starting with an interoperable database across the whole design. Cadence has put significant investment (20 years) into the OpenAccess standard and can natively read and write both digital and analog circuitry to this database. From this they can extract a full chip flat digital network structure – even for the analog components. From that it should be possible in principle to run all the standard digital signoff techniques: SDC constraints, clock and reset domain crossings, lint checks, connectivity checking, even formal checking. Paul stressed that proving and optimizing these flows is a step yet to be taken 😀

He sees the NoC challenge as something that should be perfect for formal methods. The Jasper group has been thinking about architectural formal verification for many years since these kinds of problem cannot be solved with simulation. Given the refined focus this challenge presents, the problem should be very tractable. (Editor sidenote. This problem class reminds me of formal application to cache coherence control verification, or SDN verification where you abstract away the payload and just focus on control behavior.)

Finally on PVT-related multicycle paths, Paul confessed he was stumped at least for now. A problem that only occurs on a corner that wasn’t tested is very hard to find. He closed by admitting that while automated verification will never be able to find everything, the industry will continue to push on boundaries wherever it can.

Good discussion and good input for more Innovation in Verification topics!

Also Read:

BDD-Based Formal for Floating Point. Innovation in Verification

Photonic Computing – Now or Science Fiction?

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Unleash the Power: NVIDIA GPUs, Ansys Simulation

Unleash the Power: NVIDIA GPUs, Ansys Simulation
by Daniel Nenni on 03-19-2024 at 10:00 am

Electromagnatic PerceiveEM

ANSYS Perceive EM GPU solver for 5G/6G simulations integrated into NVIDIA Omniverse

In the realm of engineering simulations, the demand for faster, more accurate solutions to complex multiphysics challenges is ever-growing.

Simulation is a vital tool for engineers to design, test, and optimize complex systems and products. It helps engineers reduce costs, improve quality, and accelerate innovation. However, the associated high computational demands, large data sets, and multiple physics domains pose significant challenges.

A key technology instrumental in meeting this demand is the graphics processing unit (GPU). With its unique applicability to acceleration of multiphysics simulations, GPUs are a game-changer driving innovation. Ansys and NVIDIA partner to deliver solutions that leverage the power of NVIDIA GPUs and the physics-based authority of Ansys software. Together, Ansys and NVIDIA are enabling engineers to solve the industry’s most computationally challenging problems.

NVIDIA GPUs for Multiphysics Simulations

NVIDIA GPUs are designed to accelerate parallel and compute-intensive tasks, such as simulations, leveraging thousands of cores and high-bandwidth memory. NVIDIA GPUs deliver magnitudes faster performance than CPUs for many simulation applications, enabling engineers to run more simulations in less time and with higher fidelity.

NVIDIA GPUs, now famous for driving the AI revolution, are renowned for their high-performance computing capabilities, making them an ideal choice for engineers and scientists working on multiphysics simulations in highly complex applications in industries such as aerospace, automotive, biomedical, and energy. These simulations account for the interaction of multiple physical phenomena, such as fluid dynamics, structural mechanics, and electromagnetics. By harnessing the parallel processing power of NVIDIA GPUs, engineers can significantly reduce simulation times and achieve more accurate results. More simulation iterations lead to more ideas and ultimately superior end products.

The innovation of 3D-IC designs requires the addition of multiphysics simulations to semiconductor design, such as electromagnetic, thermal, and structural analysis. Stacking of chiplets in close proximity within a single package brings system-level multiphysics challenges into IC design.

Solving for Multiphysics Challenges

Ansys has long been at the forefront of providing cutting-edge simulation software solutions for engineers and researchers worldwide. With a strong focus on multiphysics and semiconductor simulations, Ansys has established itself as the leader in enabling users to simulate a wide range of physical phenomena accurately. Ansys software is trusted by tens of thousands of engineers worldwide who rely on its accuracy, reliability, and scalability.

Ansys software is particularly renowned for its multiphysics simulations enabling engineers to simulate the complex interactions among various physical processes and gain valuable insights into the behavior of their systems. Ansys offers a comprehensive suite of tools for multiphysics simulations, such as Ansys Discovery, Ansys Fluent™, Ansys HFSS™, Ansys LS-DYNA™, and Ansys SPEOS™. These tools enable engineers to perform interactive, real-time, and high-fidelity simulations of various multiphysics phenomena, such as fluid-structure interaction, electromagnetic, shock and impact, and optical performance.

ANSYS LS-Dyna crash test simulation result visualized with NVIDIA Omniverse

Ansys Product Support for NVIDIA Processors: Leveraging Grace and Hopper

Ansys harnesses NVIDIA H100 GPUs to boost multiple simulation solutions and prioritizes NVIDIA’s latest processors Grace and Hopper along with the newly announced Blackwell architecture for products across the Ansys portfolio, such as Fluent, and LS-DYNA. By utilizing these products in conjunction with NVIDIA processors, engineers achieve faster simulation times, increased accuracy, and improved productivity in their work.

Ansys software can leverage the features and benefits of NVIDIA processors, such as:

  • Massive parallelism and high-bandwidth memory enable faster and more accurate simulations leading to better end products.
  • Unified memory and NVLink enable seamless data transfer and communication between CPU and GPU.
  • Tensor cores and ray tracing cores enable advanced simulations of artificial intelligence and optical effects.
  • Multi-GPU and multi-node support enable scalable simulations of large and complex models.

Gearbox CFD simulation using ANSYS Fluent

Driving Innovation: Benchmarks and Performance Gains

Ansys integrates support for NVIDIA processors into its flagship products to harness the immense potential of NVIDIA processors in enhancing simulation performance. This collaboration between Ansys and NVIDIA unlocks new possibilities for engineers seeking to leverage the power of GPU acceleration in their simulations. Ansys has already announced intent to support NVIDIA’s just announced innovative Blackwell architecture, presaging even more magnitudes of simulation acceleration

ANSYS Perceive EM GPU solver for 5G/6G simulations integrated into NVIDIA Omniverse

  • ANSYS Perceive EM GPU solver for 5G/6G simulations integrated into NVIDIA Omniverse

Compared to traditional computing methods, benchmarks demonstrate that Ansys simulations run on NVIDIA GPUs deliver significant performance gains. Engineers see a substantial reduction in simulation times, allowing for faster design iterations and more efficient problem-solving. For example:

  • Fluent enables high-fidelity and scalable fluid dynamics simulations on NVIDIA GPUs, allowing engineers to solve challenging problems such as turbulence and combustion phenomena. Fluent runs up to 5x faster on one NVIDIA H100 GPU than on dual 64 cores of a recently released high-end CPU processor.
  • Ansys Mechanical™ enables fast and accurate structural mechanics simulations on NVIDIA GPUs, allowing engineers to model complex phenomena such as acoustics, vibration, and fracture dynamics. Mechanical’s matrix kernel in Mechanical runs on 4 CPU cores up to 11x faster when adding one NVIDIA H100 GPU.
  • Ansys SPEOS enables realistic and high-performance optical simulations on NVIDIA GPUs, allowing engineers to design, measure and assess light propagation in any environment. SPEOS can run optical simulations up to 35x faster on an NVIDIA RTX™ 6000 Ada than on a recently released  8-core processor.
  • Ansys Lumerical enables comprehensive and efficient photonics simulations, allowing engineers to design and optimize photonic devices and circuits. Ansys Lumerical FDTD running on a single NVIDIA A100 GPU solves up to 40% faster than when compared to an HPC cluster containing 480 AMD EPYC 7V12 cores of a recently released high-end CPU. This equates to nearly a 6x improvement in price-performance ratio.
  • Other Ansys products such as RedHawk-SC™, Discovery, Ensight™, Rocky™, HFSS SBR+™, Perceive EM™, Maxwell™, AVxcelerate Sensors™, and RF Channel Modeler™ either already or soon will benefit from NVIDIA GPU acceleration.

Caption: ANSYS AVxcelerate Sensors (Physics-based Radar, Lidar, Camera) connected to NVIDIA Drive Sim

Embracing the Future: Grace, Hopper and now Blackwell

The future of simulation is bright, and NVIDIA’s latest innovations including NVIDIA Grace CPU, NVIDIA Hopper GPU, and now Blackwell architectures promise even more impressive performance gains. Ansys is committed to optimizing its software for these next-generation platforms, ensuring engineers have access to the most powerful simulation tools available.

The combination of Ansys’ advanced simulation software and NVIDIA’s GPU technology is revolutionizing engineers’ approach to multiphysics simulations. The recently announced expanded partnership between the two companies promises even greater advances, enhanced by artificial intelligence. Using Ansys software with NVIDIA GPUs, engineers tackle complex multiphysics simulations with unprecedented speed and accuracy, paving the way for new innovations and breakthroughs in engineering and science.

Ansys and NVIDIA Pioneer Next Era of Computer-Aided Engineering  

*CoPilot & ChatGPT, both powered by NVIDIA GPUs, contributed to this blog post.

Also Read:

Ansys and Intel Foundry Direct 2024: A Quantum Leap in Innovation

Why Did Synopsys Really Acquire Ansys?

Will the Package Kill my High-Frequency Chip Design?


Synopsys Enhances PPA with Backside Routing

Synopsys Enhances PPA with Backside Routing
by Mike Gianfagna on 03-19-2024 at 6:00 am

Comparison of frontside and backside PDNs (Source IMEC)

Complexity and density conspire to make power delivery very difficult for advanced SoCs. Signal integrity, power integrity, reliability and heat can seem to present unsolvable problems when it comes to efficient power management. There is just not enough room to get it all done with the routing layers available on the top side of the chip. A strategy is emerging to deal with the problem that seems to take a page out of the multi-die playbook. Rather than deal with the existing, single surface constraints, why not move power delivery to the backside of the chip, and get additional PPA benefit out of it? The entire fab and process equipment ecosystem is buzzing about this approach. But what about the design methodology? There is help on the way. A very informative white paper is now available from the leading EDA supplier. Read on to get the details about how Synopsys enhances PPA with backside routing.

Why Use BackSide Routing?

In a typical SOC, dedicated power layers tend to be thicker, with wider traces than the signal layers to reduce the amount of loss due to IR drop. The power delivery network, or PDN, is what brings power to all parts of the chip. PDN design required extensive analysis of electromigration, noise, and cross-coupling effects as well as IR drop to ensure power integrity. Solving this problem by adding metal layers will increases the cost and complexity of the fabrication process, if it’s even possible given process constraints.

There is more to the story which is explained well in the white paper (a link is coming). Now that manufacturing technology supports it, backside routing for the PDN is a great way to remove the obstacles, opening a new approach to implementation and the opportunity to enhance PPA. The graphic at the top of this post provides a comparison of frontside and backside PDNs. Thanks to IMEC for this depiction.

But, as they say, there’s no free lunch. For backside routing, the design process has to deal with many new problems, such as:

  • Signal integrity
    • Frontside PDN acted as a natural shield for signal integrity
    • Important to have close correlation between pre-route and post-route
  • Thermal impact
    • Thermal aware implementation to reduce impact of backside metal
  • Post-silicon observability
    • Methodologies to support robust observability
  • Multi-die and backside metal
    • Lots of synergy between the two
    • Leverage EDA technology pieces from each other

The white paper also explains what Synopsys is doing for backside routing. Here is a summary.

What Synopsys is Doing

Synopsys has embraced the use of backside routing for the PDN. The approach fits well with its design technology co-optimization (DTCO) methodology. The company has added support for backside PDNs in all relevant EDA products. The result is fast and efficient technology exploration, design PPA assessment, and design closure to accelerate the overall development process. As with many of its programs, the approach allows chip designers to adopt new silicon technology with predictable results.

A large number of additions are part of Synopsys Fusion Compiler, the industry-leading RTL-to-GDSII implementation system. The figure below summarizes the enhancement at a high level. The white paper goes into more detail about these enhancements and the measurable impact on chip design results.

Overview of Synopsys Fusion Compiler Enhancements

The white paper also discusses potential future additions to expand the use of backside routing even further.

To Learn More

Backside routing is here. The foundry ecosystem is delivering this capability and design teams need an enhanced flow to take advantage of the benefits, both today and tomorrow. Synopsys is at the leading edge of this trend and the new white paper provides important details. You can get a copy of the new Synopsys white paper here. And that’s how Synopsys enhances PPA with backside routing.


Afraid of mesh-based clock topologies? You should be

Afraid of mesh-based clock topologies? You should be
by Daniel Payne on 03-18-2024 at 10:00 am

mesh-based clock topology

Digital logic chips synchronize all logic operations by using a clock signal connected to flip-flops or latches, and the clock is distributed across the entire chip. The ultimate goal is to have a clock signal that arrives at the exact same moment in time at all clocked elements. If the clock arrives too early or too late from the PLL output to flip-flop or latch across the chip, then that time difference will impact the critical path delays and the maximum achievable clock frequency. An architect or RTL designer looks at the clock as being a perfectly defined square wave with no delays, while engineers doing timing analysis or physical design know that clock signals are starting to look more like Sine waves than square waves, and that there are delays along the clock tree depending on the topology of the clock network. At small process nodes the On-Chip Variations (OCV) make delays in logic and clock networks differ from ideal conditions, so clock designers resort to adding timing margins.

Two popular clock topologies are tree and mesh, so a comparison reveals the differences between each.

Tree and Mesh Clock topologies. Source: GLVLSI 10, May 16-18, 2010

Tree Mesh
Shared path depth Higher Lower
Timing Analysis Static Timing Analysis SPICE
Power Lower Higher
OCV More sensitive More tolerant
Clock speed Lower Higher
Clock skew Higher Lower
Routing resources Lower Higher

With a tree topology the EDA tool flow is quite automated with Clock Tree Synthesis (CTS) in popular tool flows that include logic synthesis tied to place and route, with timing analysis run on a Static Timing Analysis (STA) tool. The downsides to a tree topology are the sensitivity to OCV, lower clock speeds and higher clock skew. In modern process nodes the aging of P and N channel devices will change the duty cycle of the clock, so it may not be 50% high and 50% low, which then impacts critical path delays.

The mesh topology for clocks provides the highest clock speeds, higher tolerance to OCV and lowest clock skew. A mesh topology has downsides of higher routing resources, higher power consumption and the SPICE requirement for timing analysis. An STA tool cannot be used to analyze a mesh, because with a mesh the clock signal has paths that combine. The only information that a STA tool could provide in a mesh-based clock design is setup and hold analysis, but not critical path analysis.

There is also a middle ground, where the best aspects of tree and mesh topologies are combined, so there are choices for clock topology that are driven by your product requirements.

SPICE circuit simulation using an extracted IC netlist with parasitics is required for timing analysis of mesh-based clock topologies. For accurate analysis of OCV effects a Monte Carlo simulation using SPICE is required, and that is a very time-consuming step, plus your SPICE simulator may not have the capacity for such a large, extracted netlist. If your chip design group is intimidated by using SPICE for analyzing clock timing in a mesh-based topology, then there’s some good news, because the EDA vendor Infinisim has an easy to use product called ClockEdge that is used for clock timing analysis without you having to be a SPICE expert. The analysis provided with ClockEdge will help your team implement a mesh-based clocking topology the quickest, and with the least amount of training.

Summary

SoC designers tackle many technical issues to reach their Power, Performance and Area (PPA) goals, and choosing a clock topology is one of these issues. Most modern chip teams will be attracted to the benefits of using a combined tree and mesh topology for their clock network, as it provides high clock rates, low skew, acceptable routing resources, and withstands OCV effects. The timing analysis of mesh-based clock networks is now simplified by using the ClockEdge tool from Infinisim, as it provides analysis for metrics like: rail to rail failures, duty cycle distortion, slew rate and transition distortion, power-supply induced jjitter.

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