Overview of Machine Learning Algorithms Used In Hardware Security (TU Delft)


A new technical paper titled "A Survey on Machine Learning in Hardware Security" was published by researchers at TU Delft. Abstract "Hardware security is currently a very influential domain, where each year countless works are published concerning attacks against hardware and countermeasures. A significant number of them use machine learning, which is proven to be very effective in ... » read more

Google’s TPU v4 Architecture: 3 Major Features


A new technical paper titled "TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings" was published by researchers at Google. Abstract: "In response to innovations in machine learning (ML) models, production workloads changed radically and rapidly. TPU v4 is the fifth Google domain specific architecture (DSA) and its third supercomputer f... » read more

Combination of AI Techniques To Find The Best Ways to Place Transistors on Silicon Chips


A new technical paper titled "AutoDMP: Automated DREAMPlace-based Macro Placement" was published by researchers at NVIDIA. Abstract: "Macro placement is a critical very large-scale integration (VLSI) physical design problem that significantly impacts the design power-performance-area (PPA) metrics. This paper proposes AutoDMP, a methodology that leverages DREAMPlace, a GPU-accelerated place... » read more

AI Becoming More Prominent In Chip Design


Semiconductor Engineering sat down to talk about the role of AI in managing data and improving designs, and its growing role in pathfinding and preventing silent data corruption, with Michael Jackson, corporate vice president for R&D at Cadence; Joel Sumner, vice president of semiconductor and electronics engineering at National Instruments; Grace Yu, product and engineering manager at Meta... » read more

Hardware Based Monitoring For Zero Trust Environments


A technical paper titled "Towards Hardware-Based Application Fingerprinting with Microarchitectural Signals for Zero Trust Environments" was published by the Air Force Institute of Technology. Abstract "The interactions between software and hardware are increasingly important to computer system security. This research collects sequences of microprocessor control signals to develop machine ... » read more

Looking Beyond TOPS/W: How To Really Compare NPU Performance


There is a lot more to understanding the true capabilities of an AI engine beyond TOPS per watt. A rather arbitrary measure of the number of operations of an engine per unit of power, the TOPS/W metric completely misses the point that a single operation on one engine may accomplish more useful work than a multitude of operations on another engine. In any case, TOPS/W is by no means the only spe... » read more

How AI Drives Faster Verification Coverage And Debug For First-Time-Right Silicon


By Taruna Reddy and Robert Ruiz These days, the question is less about what AI can do and more about what it can’t do. From talk-of-the-town chatbots like ChatGPT to self-driving cars, AI is becoming pervasive in our everyday lives. Even industries where it was perhaps an unlikely fit, like chip design, are benefiting from greater intelligence. What if one of the most laborious, time-co... » read more

AI Benchmarks Are Broken


Artificial Intelligence (AI) is shaping up to be one of the most revolutionary technologies of our time. By now you’ve probably heard that AI’s impact will transform entire industries, from healthcare to finance to entertainment, delivering us richer products, streamlined experiences, and augment human productivity, creativity, and leisure. Even non-technologists are getting a glimpse of... » read more

FPGAs: Automated Framework For Architecture-Space Exploration of Approximate Accelerators


A technical paper titled "autoXFPGAs: An End-to-End Automated Exploration Framework for Approximate Accelerators in FPGA-Based Systems" was published (preprint) by researchers at TU Wien, Brno University of Technology, and NYUAD. Abstract "Generation and exploration of approximate circuits and accelerators has been a prominent research domain exploring energy-efficiency and/or performance... » read more

Spark On AWS Graviton2 Best Practices: K-Means Clustering Case Study


This report focuses on how to tune a Spark application to run on a cluster of instances. We define the concepts for the cluster/Spark parameters, and explain how to configure them given a specific set of resources. We use a K-Means machine learning algorithm as a case study to analyze and tune the parameters to achieve the required performance while optimally using the available resources. W... » read more

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