On the Viability of using LLMs for SW/HW Co-Design: An Example in
  Designing CiM DNN Accelerators
Zheyu Yan Yifan Qin Xiaobo Sharon Hu Yiyu Shi
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.