Back in 2020 I first learned from Synopsys about how they had engineered a better way to do optimize layouts on digital designs by using machine learning techniques, instead of relying upon manual approaches. The product was named DSO.ai, standing for Design Space Optimization, and it produced a more optimal floor-plan in less… Read More
Tag: machine learning
An FPGA-Based Solution for a Graph Neural Network (GNN) Accelerator
Earlier this year, Achronix made a product announcement about shipping the industry’s highest performance Speedster7t FPGA devices. The press release included lot of details about the architecture and features of the device and how that family of devices is well suited to satisfy the demands of the artificial intelligence … Read More
Cerebrus, the ML-based Intelligent Chip Explorer from Cadence
Electronic design automation (EDA) has come a long way from its beginnings. It has enabled chip engineers from specifying designs directly in layout format during the early days to today’s capture in RTL format. Every advance in EDA has made the task of designing a chip easier and increased the design team productivity, enabling… Read More
Siemens EDA is Applying Machine Learning to Back-End Wafer Processing Simulation
There’s a lot to unpack in the title of this post. First, Siemens EDA is the new name for Mentor, a Siemens Business. The organization continues to operate as part of Siemens Digital Industries Software. The organization has released a white paper that describes research done with the American University of Armenia. The work examines… Read More
Tempus: Delivering Faster Timing Signoff with Optimal PPA
In July, I explored the benefits of the new Cadence Tempus™ Power Integrity Solution. In that piece, I explored some of the unique capabilities of this new tool with Brandon Bautz, senior product management group director and Hitendra Divecha, product management director in the Digital & Signoff Group at Cadence. I recently… Read More
Alchip at TSMC OIP – Reticle Size Design and Chiplet Capabilities
This is another installment covering TSMC’s very popular Open Innovation Platform event (OIP), held on August 25. This event presents a diverse and high-impact series of presentations describing how TSMC’s vast ecosystem collaborates with each other and with TSMC. This presentation is from Alchip, presented by James Huang,… Read More
Cadence Increases Verification Efficiency up to 5X with Xcelium ML
SoC verification has always been an interesting topic for me. Having worked at companies like Zycad that offered hardware accelerators for logic and fault simulation, the concept of reducing the time needed to verify a complex SoC has occupied a lot of my thoughts. The bar we always tried to clear was actually simple to articulate… Read More
DAC Panel: Cadence Weighs in on AI for EDA, What Applications, Where’s the Data?
DAC was full of great panels, research papers and chip design stories this year, the same as other years. Being a virtual show, there were some differences of course. I’ve heard attendance was way up, allowing a lot more folks to experience the technical program. This is likely to be true for a virtual event. I’m sure we’ll see more… Read More
The Future of Chip Design with the Cadence iSpatial Flow
A few months ago, I wrote about the announcement of a new digital full flow from Cadence. In that piece, I focused on the machine learning (ML) aspects of the new tool. I had covered a discussion with Cadence’s Paul Cunningham a week before that explored ML in Cadence products, so it was timely to dive into a real-world example of the … Read More
A Compelling Application for AI in Semiconductor Manufacturing
There have been a multitude of announcements recently relative to the incorporation of machine learning (ML) methods into EDA tool algorithms, mostly in the physical implementation flows. For example, deterministic ML-based decision algorithms applied to cell placement and signal interconnect routing promise to expedite… Read More