tag: LLMs

LLMs Show Promise In Secure IC Design
By Adam Kovac - 21 Oct, 2024 - Comments: 0

The introduction of large language models into the EDA flow could significantly reduce the time, effort, and cost of designing secure chips and systems, but they also could open the door to more sophisticated attacks. It's still early days for the use of LLMs in chip and system design. The technology is just beginning to be implemented, and there are numerous technical challenges that must b... » read more

Mass Customization For AI Inference
By Ann Mutschler - 17 Oct, 2024 - Comments: 0

Rising complexity in AI models and an explosion in the number and variety of networks is leaving chipmakers torn between fixed-function acceleration and more programmable accelerators, and creating some novel approaches that include some of both. By all accounts, a general-purpose approach to AI processing is not meeting the grade. General-purpose processors are exactly that. They're not des... » read more

Survey: HW SW Co-Design Approaches Tailored to LLMs
By Technical Paper Link - 11 Oct, 2024 - Comments: 0

A new technical paper titled "A Survey: Collaborative Hardware and Software Design in the Era of Large Language Models" was published by researchers at Duke University and Johns Hopkins University. Abstract "The rapid development of large language models (LLMs) has significantly transformed the field of artificial intelligence, demonstrating remarkable capabilities in natural language proce... » read more

Novel NorthPole Architecture Enables Low-Latency, High-Energy-Efficiency LLM inference (IBM Research)
By Technical Paper Link - 27 Sep, 2024 - Comments: 0

A new technical paper titled "Breakthrough low-latency, high-energy-efficiency LLM inference performance using NorthPole" was published by researchers at IBM Research. At the IEEE High Performance Extreme Computing (HPEC) Virtual Conference in September 2024, new performance results for their AIU NorthPole AI inference accelerator chip were presented on a 3-billion-parameter Granite LLM. ... » read more

Using AI To Glue Disparate IC Ecosystem Data
By Ed Sperling and Ann Mutschler - 26 Sep, 2024 - Comments: 0

AI holds the potential to change how companies interact throughout the global semiconductor ecosystem, gluing together different data types and processes that can be shared between companies that in the past had little or no direct connections. Chipmakers always have used abstraction layers to see the bigger picture of how the various components of a chip go together, allowing them to pinpoi... » read more

Scalable Chiplet System for LLM Training, Finetuning and Reduced DRAM Accesses (Tsinghua University)
By Technical Paper Link - 04 Sep, 2024 - Comments: 0

A new technical paper titled "Hecaton: Training and Finetuning Large Language Models with Scalable Chiplet Systems" was published by researchers at Tsinghua University. Abstract "Large Language Models (LLMs) have achieved remarkable success in various fields, but their training and finetuning require massive computation and memory, necessitating parallelism which introduces heavy communicat... » read more

LLMs In The High-Level Synthesis Design Flow
By Technical Paper Link - 27 Aug, 2024 - Comments: 0

A new technical paper titled "Are LLMs Any Good for High-Level Synthesis?" was published by researchers at University of Arizona. Abstract "The increasing complexity and demand for faster, energy-efficient hardware designs necessitate innovative High-Level Synthesis (HLS) methodologies. This paper explores the potential of Large Language Models (LLMs) to streamline or replace the HLS proces... » read more

Prevent AI Hardware Obsolescence And Optimize Efficiency With eFPGA Adaptability
By Jayson Bethurem - 01 Aug, 2024 - Comments: 0

Large Language Models (LLMs) and Generative AI are driving up memory requirements, presenting a significant challenge. Modern LLMs can have billions of parameters, demanding many gigabytes of memory. To address this issue, AI architects have devised clever solutions that dramatically reduce memory needs. Evolving techniques like lossless weight compression, structured sparsity, and new numer... » read more

Capturing Knowledge Within LLMs
By Brian Bailey - 25 Jul, 2024 - Comments: 0

At DAC this year, there was a lot of talk about AI and the impact it is likely to have. While EDA companies have been using it for optimization and improving iteration loops within the flow, the end users have been concentrating on how to use it to improve the user interface between engineers and tools. The feedback is very positive. Large language models (LLMs) have been trained on a huge a... » read more

224Gbps PHY For The Next Generation Of High Performance Computing
By Priyank Shukla - 18 Jul, 2024 - Comments: 0

Large language models (LLMs) are experiencing an explosive growth in parameter count. Training these ever-larger models requires multiple accelerators to work together, and the bandwidth between these accelerators directly limits the size of trainable LLMs in High Performance Computing (HPC) environments. The correlation between the LLM size and data rates of interconnect technology herald a... » read more

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