From Data Center To End Device: AI/ML Inferencing With GDDR6


Created to support 3D gaming on consoles and PCs, GDDR packs performance that makes it an ideal solution for AI/ML inferencing. As inferencing migrates from the heart of the data center to the network edge, and ultimately to a broad range of AI-powered IoT devices, GDDR memory’s combination of high bandwidth, low latency, power efficiency and suitability for high-volume applications will be i... » read more

Challenges In Using AI In Verification


Pressure to use AI/ML techniques in design and verification is growing as the amount of data generated from complex chips continues to explode, but how to begin building those capabilities into tools, flows and methodologies isn't always obvious. For starters, there is debate about whether the data needs to be better understood before those techniques are used, or whether it's best to figure... » read more

Artificial Intelligence And Machine Learning Add New Capabilities to Traditional RF EDA Tools


This article features contributions from RF EDA vendors on their various capabilities for artificial intelligence and machine learning. AWR Design Environment software is featured and highlights the network synthesis wizard. Click here to continue reading. » read more

Finding Defects With E-Beam Inspection


Several companies are developing or shipping next-generation e-beam inspection systems in an effort to reduce defects in advanced logic and memory chips. Vendors are taking two approaches with these new e-beam inspection systems. One is a more traditional approach, which uses a single-beam e-beam system. Others, meanwhile, are developing newer multi-beam technology. Both approaches have thei... » read more

Scaling AI/ML Training Performance With HBM2E Memory


In my April SemiEngineering Low Power-High Performance blog, I wrote: “Today, AI/ML neural network training models can exceed 10 billion parameters, soon it will be over 100 billion.” “Soon” didn’t take long to arrive. At the end of May, OpenAI unveiled a new 175-billion parameter GPT-3 language model. This represented a more that 100X jump over the size of GPT-2’s 1.5 billion param... » read more

Customization And Limitations At The Edge


Semiconductor Engineering sat down to discuss the edge constraints and the need for security with Jeff DeAngelis, managing director of the Industrial and Healthcare Business Unit at Maxim Integrated; Norman Chang, chief technologist at Ansys; Andrew Grant, senior director of artificial intelligence at Imagination Technologies; Thomas Ensergueix, senior director of the automotive and IoT line of... » read more

Big Changes In AI Design


Semiconductor Engineering sat down to discuss AI and its move to the edge with Steven Woo, vice president of enterprise solutions technology and distinguished inventor at Rambus; Kris Ardis, executive director at Maxim Integrated; Steve Roddy, vice president of Arm's Products Learning Group; and Vinay Mehta, inference technical marketing manager at Flex Logix. What follows are excerpts of that ... » read more

Week In Review: Auto, Security, Pervasive Computing


Pervasive computing — data center, edge, IoT, 5G Qualcomm settled its 5G licensing disagreement with Huawei, which will pay $1.8 billion in back royalties and will pay for licensing going forward. Huawei is also now the world’s largest supplier of smartphones, surpassing Samsung Electronics Co. Qualcomm also announced a super-fast charging platform this week for Android devices that is sup... » read more

Engineering Within Constraints


One of the themes of DAC this year was the next phase of machine learning. It is as if CNNs and RNNs officially have migrated from the research community and all that is left now is optimization. The academics need something new. Quite correctly, they have identified power as the biggest problem associated with learning and inferencing today, and a large part of that problem is associated with ... » read more

Machine Learning — Everywhere: Enabling Self-Optimizing Design Platforms


Machine-learning offers opportunities to enable self-optimizing design tools. Very much like self-driving cars that observe real-world interactions to improve their responses in different (local) driving conditions, AI-enhanced tools are able to learn and improve in (local) design environments after deployment. These new, ML-driven capabilities can be embedded in different design engines, gi... » read more

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