Insights From The AI Hardware & Edge AI Summit


By Ashish Darbari, Fabiana Muto, and Nicky Khodadad In today's rapidly changing technology landscape, artificial intelligence (AI) is more than a buzzword. It is transforming businesses and societies. From advances in scalable AI methodology to urgent calls for sustainability, the AI Hardware & Edge AI Summit recently held in London, sparked vibrant discussions that will determine the fu... » read more

Changes In Formal Verification


For the better part of two decades, formal verification was considered too difficult to use in many designs and too slow for anything but narrow bug hunting. Much has changed recently. Ashish Darbari, CEO of Axiomise, explains why formal is now essential for finding deadlocks, security holes, and Xprop issues in mission-critical, safety-critical, and AI designs, and how that will apply to chipl... » read more

Verification Tools Straining To Keep Up


Verification engineers are the unsung heroes of the semiconductor industry, but they are at a breaking point and desperately in need of modern tools and flows to deal with the rapidly increasing pressures. Verification is no longer just about ensuring that functionality is faithfully represented in an implementation. That alone is an insolvable task, but verification has taken on many new re... » read more

RISC-V Verification: From Simulation To Formal


Axiomise's Nicky Khodadad and Ashish Darbari discuss simulation and the need for formal verification and RISC-V, including why simulation-based verification is inadequate to find all the bugs in a design and how formal verification can help with bug hunting for corner-case bugs and exhaustive proofs of bug absence. » read more

Why IC Design Safety Nets Have Limits


Experts at the Table: Semiconductor Engineering sat down to discuss different responsibilities in design teams and future changes in tools with Ashish Darbari, CEO at Axiomise; Ziyad Hanna, corporate vice president R&D at Cadence; Jim Henson, ASIC verification software product manager at Siemens EDA; Dirk Seynhaeve, vice president of business development at Sigasi; Simon Davidmann, formerly... » read more

Chip Design Digs Deeper Into AI


Growing demand for blazing fast and extremely dense multi-chiplet systems are pushing chip design deeper into AI, which increasingly is viewed as the best solution for sifting through scores of possible configurations, constraints, and variables in the least amount of time. This shift has broad implications for the future of chip design. In the past, collaborations typically involved the chi... » read more

Trouble Ahead For IC Verification


Verification complexity is roughly the square of design complexity, but until recently verification success rates have remained fairly consistent. That's beginning to change. There are troubling signs that verification is collapsing under the load. The first-time success rate fell (see figure 1) in the last survey conducted by Wilson Research, on behalf of Siemens EDA, in 2022. A new survey ... » read more

Communication Is Key To Finding And Fixing Bugs In ICs


Experts at the Table: Finding and eliminating bugs at the source can be painstaking work, but it also can prevent even greater problems from developing later on. To examine the best ways to tackle this problem, Semiconductor Engineering sat down with Ashish Darbari, CEO at Axiomise; Ziyad Hanna, corporate vice president R&D at Cadence; Jim Henson, ASIC verification software product manager ... » read more

Multi-Die Design Pushes Complexity To The Max


Multi-die/multi-chiplet design has thrown a wrench into the ability to manage design complexity, driving up costs per transistor, straining market windows, and sending the entire chip industry scrambling for new tools and methodologies. For multiple decades, the entire semiconductor design ecosystem — from EDA and IP providers to foundries and equipment makers — has evolved with the assu... » read more

Dealing With AI/ML Uncertainty


Despite their widespread popularity, large language models (LLMs) have several well-known design issues, the most notorious being hallucinations, in which an LLM tries to pass off its statistics-based concoctions as real-world facts. Hallucinations are examples of a fundamental, underlying issue with LLMs. The inner workings of LLMs, as well as other deep neural nets (DNNs), are only partly kno... » read more

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