Shrinking LLMs With Self-Compression


Language models are becoming ever larger, making on-device inference slow and energy-intensive. A direct and surprisingly effective remedy is to prune complete channels whose contribution to the task is negligible. Our earlier work introduced a training-time procedure – Self-Compression [1, 4] – that lets back-propagation decide the bit-width of every channel, so unhelpful ones fade away. T... » read more

AI: A New Tool For Hackers, And For Preventing Attacks


Semiconductor Engineering sat down to discuss hardware security challenges, including new threat models from AI-based attacks, with Nicole Fern, principal security analyst at Keysight; Serge Leef, AI-For-Silicon strategist at Microsoft; Scott Best, senior director for silicon security products at Rambus; Lee Harrison, director of Tessent Automotive IC Solutions at Siemens EDA; Mohit Arora, seni... » read more

LLM-Powered Automatic VLSI Design Flow Tuning Framework


A new technical paper titled "CROP: Circuit Retrieval and Optimization with Parameter Guidance using LLMs" was published by researchers at Duke University and Synopsys. Abstract "Modern very large-scale integration (VLSI) design requires the implementation of integrated circuits using electronic design automation (EDA) tools. Due to the complexity of EDA algorithms, the vast parameter space... » read more

Detailed Study of Performance Modeling For LLM Implementations At Scale (imec)


A new technical paper titled "System-performance and cost modeling of Large Language Model training and inference" was published by researchers at imec. Abstract "Large language models (LLMs), based on transformer architectures, have revolutionized numerous domains within artificial intelligence, science, and engineering due to their exceptional scalability and adaptability. However, the ex... » read more

HW Security: Multi-Agent AI Assistant Leveraging LLMs To Automate Key Stages of SoC Security Verification (U. of Florida)


A new technical paper titled "SV-LLM: An Agentic Approach for SoC Security Verification using Large Language Models" was published by researchers at University of Florida. Abstract "Ensuring the security of complex system-on-chips (SoCs) designs is a critical imperative, yet traditional verification techniques struggle to keep pace due to significant challenges in automation, scalability, c... » read more

LLMs On The Edge


Nearly all the data input for AI so far has been text, but that's about to change. In the future, that input likely will include video, voice, as well as other types of data, causing a massive increase in the amount of data that needs to be modeled and the compute resources necessary to make it all work. This is hard enough in hyperscale data centers, which are sprouting up everywhere to handle... » read more

Fully Automated Hardware And Software Design Of Processor Chips (Chinese Academy Of Sciences)


A new technical paper titled "QiMeng: Fully Automated Hardware and Software Design for Processor Chip" was published by researchers at Chinese Academy of Sciences. Abstract "Processor chip design technology serves as a key frontier driving breakthroughs in computer science and related fields. With the rapid advancement of information technology, conventional design paradigms face three majo... » read more

Agentic AI In Chip Design


Large language models (LLMs) like ChatGPT are just the starting point for generating content with AI. The next phase will be about harnessing LLMs with agents, providing automated feedback and improvements in performance and accuracy. Mehir Arora, backend engineer at ChipAgents, talks about the impact this can have on EDA and chip design, allowing smaller teams to compete with larger teams, and... » read more

Evaluation of LLMs on HDL-Based Communication Protocol Generation (U. of Illinois Urbana, CISPA)


A new technical paper titled "ProtocolLLM: RTL Benchmark for SystemVerilog Generation of Communication Protocols" was published by researchers at University of Illinois Urbana Champaign and CISPA Helmholtz Center for Information Security. Abstract "Recent advances in Large Language Models (LLMs) have shown promising capabilities in generating code for general-purpose programming languages. ... » read more

Roadmap for AI HW Development, With The Role of Photonic Chips In Supporting Future LLMs (CUHK, NUS, UIUC, Berkeley)


A new technical paper titled "What Is Next for LLMs? Next-Generation AI Computing Hardware Using Photonic Chips" was published by researchers at The Chinese University of Hong Kong, National University of Singapore, University of Illinois Urbana-Champaign and UC Berkeley. Abstract "Large language models (LLMs) are rapidly pushing the limits of contemporary computing hardware. For example, t... » read more

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