A Dataset for Large Language Model-Driven AI Accelerator Generation
arxiv(2024)
摘要
In the ever-evolving landscape of Deep Neural Networks (DNN) hardware
acceleration, unlocking the true potential of systolic array accelerators has
long been hindered by the daunting challenges of expertise and time investment.
Large Language Models (LLMs) offer a promising solution for automating code
generation which is key to unlocking unprecedented efficiency and performance
in various domains, including hardware descriptive code. However, the
successful application of LLMs to hardware accelerator design is contingent
upon the availability of specialized datasets tailored for this purpose. To
bridge this gap, we introduce the Systolic Array-based Accelerator DataSet
(SA-DS). SA-DS comprises of a diverse collection of spatial arrays following
the standardized Berkeley's Gemmini accelerator generator template, enabling
design reuse, adaptation, and customization. SA-DS is intended to spark
LLM-centred research on DNN hardware accelerator architecture. We envision that
SA-DS provides a framework which will shape the course of DNN hardware
acceleration research for generations to come. SA-DS is open-sourced under the
permissive MIT license at this https://github.com/ACADLab/SA-DS.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要