A technical paper titled “On the Viability of using LLMs for SW/HW Co-Design: An Example in Designing CiM DNN Accelerators” was published by researchers at University of Notre Dame.
“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.”
Find the technical paper here. Published: June 2023 (preprint).
Yan, Zheyu, Yifan Qin, Xiaobo Sharon Hu, and Yiyu Shi. “On the Viability of using LLMs for SW/HW Co-Design: An Example in Designing CiM DNN Accelerators.” arXiv preprint arXiv:2306.06923 (2023).
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