tag: large language models

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

PCIe 7.0: Speed, Flexibility & Efficiency For The AI Era
By Lou Ternullo - 18 Jul, 2024 - Comments: 0

As the industry came together for PCI-SIG DevCon last month, one thing took center stage, and that was PCI Express 7.0. While still in the final stages of development, the world is certainly ready for this significant new milestone of the PCIe specification. Let’s look at how PCIe 7.0 is poised to address the escalating demands of AI, high-performance computing, and emerging data-intensive ap... » read more

Language’s Role In Embodied Agents
By Ben Gomes - 27 Jun, 2024 - Comments: 0

Large Language Models (LLMs) and models cross-trained on natural language are a major growth area for edge applications of neural networks and Artificial Intelligence (AI). Within the spectrum of applications, embodied agents stand out as a major developing focal point for this AI. This article will address developments in this space and how the application of language-trained models improves t... » read more

Vision Is Why LLMs Matter On The Edge
By Ben Gomes - 30 May, 2024 - Comments: 0

Large Language Models (LLMs) have taken the world by storm since the 2017 Transformers paper, but pushing them to the edge has proved problematic. Just this year, Google had to revise its plans to roll out Gemini Nano on all new Pixel models — the down-spec’d hardware options proved unable to host the model as part of a positive user experience. But the implementation of language-focused mo... » read more

Scheduling Multi-Model AI Workloads On Heterogeneous MCM Accelerators (UC Irvine)
By Technical Paper Link - 24 May, 2024 - Comments: 0

A technical paper titled “SCAR: Scheduling Multi-Model AI Workloads on Heterogeneous Multi-Chiplet Module Accelerators” was published by researchers at University of California Irvine. Abstract: "Emerging multi-model workloads with heavy models like recent large language models significantly increased the compute and memory demands on hardware. To address such increasing demands, designin... » read more

How To Successfully Deploy GenAI On Edge Devices
By Gordon Cooper - 16 May, 2024 - Comments: 0

Generative AI (GenAI) burst onto the scene and into the public’s imagination with the launch of ChatGPT in late 2022. Users were amazed at the natural language processing chatbot’s ability to turn a short text prompt into coherent humanlike text including essays, language translations, and code examples. Technology companies – impressed with ChatGPT’s abilities – have started looking ... » read more

Leveraging LLMs To Explain EDA Synthesis Errors And Help Train New Engineers 
By Technical Paper Link - 08 May, 2024 - Comments: 0

A technical paper titled “Explaining EDA synthesis errors with LLMs” was published by researchers at University of New South Wales and University of Calgary. Abstract: "Training new engineers in digital design is a challenge, particularly when it comes to teaching the complex electronic design automation (EDA) tooling used in this domain. Learners will typically deploy designs in the Veri... » read more

Paradigms Of Large Language Model Applications In Functional Verification
By Siemens EDA - 03 Apr, 2024 - Comments: 0

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

Efficient Streaming Language Models With Attention Sinks (MIT, Meta, CMU, NVIDIA)
By Technical Paper Link - 04 Mar, 2024 - Comments: 0

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

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