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There is No Easy Fix to AI Privacy Problems

There is No Easy Fix to AI Privacy Problems
by Matthew Rosenquist on 03-14-2020 at 8:00 am

There is No Easy Fix to AI Privacy Problems

Artificial intelligence – more specifically, the machine learning (ML) subset of AI – has a number of privacy problems.

Not only does ML require vast amounts of data for the training process, but the derived system is also provided with access to even greater volumes of data as part of the inference processing while in operation. … Read More


Trends in AI and Safety for Cars

Trends in AI and Safety for Cars
by Bernard Murphy on 03-03-2020 at 6:00 am

AI at the Edge

The potential for AI in cars, whether for driver assistance or full autonomy, has been trumpeted everywhere and continues to grow. Within the car we have vision, radar and ultrasonic sensors to detect obstacles in front, behind and to the side of the car. Outside the car, V2x promises to share real-time information between vehicles… Read More


Mentor Helps Mythic Implement Analog Approach to AI

Mentor Helps Mythic Implement Analog Approach to AI
by Tom Simon on 02-27-2020 at 6:00 am

Mythic AMS Verification Challenges

The entire field of Artificial Intelligence (AI) has resulted from what is called “first principles thinking”, where problems are re-examined using a complete reassessment of the underlying issues and potential solutions. It is a testament to how effective this can be that AI is being used for a rapidly expanding number of applications… Read More


Webinar – FPGA Native Block Floating Point for Optimizing AI/ML Workloads

Webinar – FPGA Native Block Floating Point for Optimizing AI/ML Workloads
by Tom Simon on 02-25-2020 at 10:00 am

block float example

Block floating point (BFP) has been around for a while but is just now starting to be seen as a very useful technique for performing machine learning operations. It’s worth pointing out up front that bfloat is not the same thing. BFP combines the efficiency of fixed point operations and also offers the dynamic range of full floating… Read More


Edge Computing – The Critical Middle Ground

Edge Computing – The Critical Middle Ground
by Mike Gianfagna on 02-21-2020 at 10:00 am

Computing hierarchy

Ron Lowman, product marketing manager at Synopsys, recently posted an interesting technical bulletin on the Synopsys website entitled How AI in Edge Computing Drives 5G and the IoT. There’s been a lot of discussion recently about the emerging processing hierarchy of edge devices (think cell phone or self-driving car), cloud… Read More


High-Level Synthesis at the Edge

High-Level Synthesis at the Edge
by Bernard Murphy on 02-19-2020 at 6:00 am

AI Traditional Hardware Solutions

Custom AI acceleration continues to gather steam. In the cloud, Alibaba has launched its own custom accelerator, following Amazon and Google. Facebook is in the game too and Microsoft has a significant stake in Graphcore. Intel/Mobileye have a strong lock on edge AI in cars and wireless infrastructure builders are adding AI capabilities… Read More


Thermal Reliability Challenges in Automotive and Data Center Applications – A Xilinx Perspective

Thermal Reliability Challenges in Automotive and Data Center Applications – A Xilinx Perspective
by Bernard Murphy on 02-13-2020 at 6:00 am

thermometer

I wrote recently on ANSYS and TSMC’s joint work on thermal reliability workflows, as these become much more important in advanced processes and packaging. Xilinx provided their own perspective on thermal reliability analysis for their unquestionably large systems – SoC, memory, SERDES and high-speed I/O – stacked within a … Read More


TinyML Makes Big Impact in Edge AI Applications

TinyML Makes Big Impact in Edge AI Applications
by Tom Simon on 02-12-2020 at 10:00 am

TimyML ECM3532 Architecture

Machine Learning (ML) has become extremely important for many computing applications, especially ones that involve interacting with the physical world. Along with this trend has come the development of many specialized ML processors for cloud and mobile applications. These chips work fine in the cloud or even in cars or phones,… Read More


Innovation in Verification – February 2020

Innovation in Verification – February 2020
by Bernard Murphy on 02-11-2020 at 6:00 am

Innovation in Verification

This blog is the next in a series in which Paul Cunningham (GM of the Verification Group at Cadence), Jim Hogan and I pick a paper on a novel idea in verification and debate its strengths and opportunities for improvement.

Our goal is to support and appreciate further innovation in this area. Please let us know what you think and please… Read More


AI Interposer Power Modeling and HBM Power Noise Prediction Studies

AI Interposer Power Modeling and HBM Power Noise Prediction Studies
by Mike Gianfagna on 02-07-2020 at 6:00 am

Picture1

I attended a session on 2.5D silicon interposer analysis at DesignCon 2020. Like many presentations at this show, ecosystem collaboration was a focus. In this session, Jinsong Hu (principal application engineer at Cadence) and Yongsong He (senior staff engineer at Enflame Tech) presented approaches for interposer power modeling… Read More