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

Hardware Fuzzer Utilizing LLMs


A new technical paper titled "Beyond Random Inputs: A Novel ML-Based Hardware Fuzzing" was published by researchers at TU Darmstadt and Texas A&M University. Abstract "Modern computing systems heavily rely on hardware as the root of trust. However, their increasing complexity has given rise to security-critical vulnerabilities that cross-layer at-tacks can exploit. Traditional hardware ... » read more

Paradigms Of Large Language Model Applications In Functional Verification


This paper presents a comprehensive literature review for applying large language models (LLM) in multiple aspects of functional verification. Despite the promising advancements offered by this new technology, it is essential to be aware of the inherent limitations of LLMs, especially hallucination that may lead to incorrect predictions. To ensure the quality of LLM outputs, four safeguarding p... » read more

LLMs For EDA, HW Design and Security


A new technical paper titled "Hardware Phi-1.5B: A Large Language Model Encodes Hardware Domain Specific Knowledge" was published by researchers at Kansas State University, University of Science and Technology of China, Michigan Technological University, Washington University in St. Louis and Silicon Assurance. Abstract "In the rapidly evolving semiconductor industry, where research, design... » read more

Efficient Streaming Language Models With Attention Sinks (MIT, Meta, CMU, NVIDIA)


A technical paper titled “Efficient Streaming Language Models with Attention Sinks” was published by researchers at Massachusetts Institute of Technology (MIT), Meta AI, Carnegie Mellon University (CMU), and NVIDIA. Abstract: "Deploying Large Language Models (LLMs) in streaming applications such as multi-round dialogue, where long interactions are expected, is urgently needed but poses tw... » read more

Generating And Evaluating HW Verification Assertions From Design Specifications Via Multi-LLMs


A technical paper titled “AssertLLM: Generating and Evaluating Hardware Verification Assertions from Design Specifications via Multi-LLMs” was published by researchers at Hong Kong University of Science and Technology. Abstract: "Assertion-based verification (ABV) is a critical method for ensuring design circuits comply with their architectural specifications, which are typically describe... » read more

Training Large LLM Models With Billions To Trillion Parameters On ORNL’s Frontier Supercomputer


A technical paper titled “Optimizing Distributed Training on Frontier for Large Language Models” was published by researchers at Oak Ridge National Laboratory (ORNL) and Universite Paris-Saclay. Abstract: "Large language models (LLMs) have demonstrated remarkable success as foundational models, benefiting various downstream applications through fine-tuning. Recent studies on loss scaling ... » read more

Unlocking The Power Of Edge Computing With Large Language Models


In recent years, Large Language Models (LLMs) have revolutionized the field of artificial intelligence, transforming how we interact with devices and the possibilities of what machines can achieve. These models have demonstrated remarkable natural language understanding and generation abilities, making them indispensable for various applications. However, LLMs are incredibly resource-intensi... » read more

A Study Of LLMs On Multiple AI Accelerators And GPUs With A Performance Evaluation


A technical paper titled “A Comprehensive Performance Study of Large Language Models on Novel AI Accelerators” was published by researchers at Argonne National Laboratory, State University of New York, and University of Illinois. Abstract: "Artificial intelligence (AI) methods have become critical in scientific applications to help accelerate scientific discovery. Large language models (L... » read more

LLMs For Hardware Design Verification


A technical paper titled “LLM4DV: Using Large Language Models for Hardware Test Stimuli Generation” was published by researchers at University of Cambridge, lowRISC, and Imperial College London. Abstract: "Test stimuli generation has been a crucial but labor-intensive task in hardware design verification. In this paper, we revolutionize this process by harnessing the power of large langua... » read more

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