On the Viability of using LLMs for SW/HW Co-Design: An Example in Designing CiM DNN Accelerators

• URL: http://arxiv.org/abs/2306.06923v1
• Date: Mon, 12 Jun 2023 07:50:53 GMT
• Title: On the Viability of using LLMs for SW/HW Co-Design: An Example in Designing CiM DNN Accelerators
• Authors: Zheyu Yan, Yifan Qin, Xiaobo Sharon Hu, Yiyu Shi
• Abstract summary: Deep Neural Networks (DNNs) have demonstrated impressive performance across a wide range of tasks. deploying DNNs on edge devices poses significant challenges due to stringent power and computational budgets. We present a novel approach that leverages Large Language Models (LLMs) to address this issue.
• Score: 14.02304927398616
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deep Neural Networks (DNNs) have demonstrated impressive performance across a wide range of tasks. However, deploying DNNs on edge devices poses significant challenges due to stringent power and computational budgets. An effective solution to this issue is software-hardware (SW-HW) co-design, which allows for the tailored creation of DNN models and hardware architectures that optimally utilize available resources. However, SW-HW co-design traditionally suffers from slow optimization speeds because their optimizers do not make use of heuristic knowledge, also known as the ``cold start'' problem. In this study, we present a novel approach that leverages Large Language Models (LLMs) to address this issue. By utilizing the abundant knowledge of pre-trained LLMs in the co-design optimization process, we effectively bypass the cold start problem, substantially accelerating the design process. The proposed method achieves a significant speedup of 25x. This advancement paves the way for the rapid and efficient deployment of DNNs on edge devices.

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