Tools Needed To Track, Catalog Hardware Vulnerabilities


Monitoring for cyberattacks is a key component of hardware-based security, but what happens afterward is equally important. Logging and cataloging identified hardware vulnerabilities to ensure they are not repeated is essential for security. In fact, thousands of weak points have been identified as part of the chip design process, and even posted publicly online. Nevertheless, many companies... » read more

The Evolution of HBM


High-bandwidth memory originally was conceived as a way to increase capacity in memory attached to a 2.5D package. It has since become a staple for all high-performance computing, in some cases replacing SRAM for L3 cache. Archana Cheruliyil, senior product marketing manager at Alphawave Semi, talks about how and where HBM is used today, how it will be used in the future, why it is essential fo... » read more

Aging, Complexity, And AI In Analog Design


Experts at the Table: Semiconductor Engineering sat down to discuss abstraction in analog vs. digital, how analog circuits age, the growing role of AI, and why there is so much margin in analog designs, with Mo Faisal, president and CEO of Movellus; Hany Elhak, executive director of product management at Synopsys; Cedric Pujol, product manager at Keysight; and Pradeep Thiagarajan, principal pro... » read more

Critical Design Considerations For High-Bandwidth Chiplet Interconnects (TSMC)


A new technical paper titled "High-Bandwidth Chiplet Interconnects for Advanced Packaging Technologies in AI/ML Applications: Challenges and Solutions" was published by researchers at TSMC. Abstract: "The demand for chiplet integration using 2.5D and 3D advanced packaging technologies has surged, driven by the exponential growth in computing performance required by Artificial Intelligence a... » read more

Goal-Driven AI


For many, the long-term dream for AI within EDA is the ability to define a set of goals and tell the computer to go design it for them. A short while later, an optimized design will pop out. All of today's EDA tools will remain hidden, if they even exist at all. You would only be limited by your imagination. But we also know that AI is not to be trusted today, especially when millions of dol... » read more

Redefining XPU Memory For AI Data Centers Through Custom HBM4: Part 1


This is the first of a three-part series on HBM4 and gives an overview of the HBM standard. Part 2 will provide insights on HBM implementation challenges, and part 3 will introduce the concept of a custom HBM implementation. Relentless growth in data consumption Recent advances in deep learning have had a transformative effect on artificial intelligence (AI) and the ever-increasing volume of ... » read more

Slow Progress On Generative EDA


Progress is being made in generative EDA, but the lack of training data remains the biggest problem. Some areas are finding ways around this. Generative AI, driven by large language models (LLMs), stormed into the world just two years ago, and since then has worked its way into almost every aspect of our lives. Some people love it, others hate it, and some even give dire warnings about machi... » read more

How AI Is Transforming System Design


Experts At The Table: ChatGPT and other LLMs have attracted most of the attention in recent years, but other forms of AI have long been incorporated into design workflows. The technology has become so common that many designers may not even realize it’s a part of the tools they use every day. But its adoption is spreading deeper into tools and methodologies. Semiconductor Engineering sat down... » read more

GenAI + Semiconductors + Humanity


Silicon Catalyst held its 2024 Semiconductor Industry Forum in Mountain View, CA, at the Computer History Museum on November 13th. Richard Curtin, managing partner for Si Catalyst, opened the event by thanking David House, vice chair of the Board at the Computer History Museum and creator of the 4004 processor, and the CHM staff for hosting the event. Richard talked about the start of se... » read more

Small Language Models: A Solution To Language Model Deployment At The Edge?


While Large Language Models (LLMs) like GPT-3 and GPT-4 have quickly become synonymous with AI, LLM mass deployments in both training and inference applications have, to date, been predominately cloud-based. This is primarily due to the sheer size of the models; the resulting processing and memory requirements often overwhelm the capabilities of edge-based systems. While the efficiency of Exped... » read more

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