ENASA: Towards Edge Neural Architecture Search based on CIM acceleration
Shixin Zhao, Songyun Qu, Ying Wang, Yinhe Han · 2023
This work proposes a ReRAM-based Computing-in-Memory (CIM) architecture for Neural Architecture Search (NAS) acceleration, ENASA, so that the compute-intensive NAS technology can be applied to various edge devices to customize the most suitable individual solution for their cases. In the popular one-shot NAS process, the system must repetitively evaluate the sampled sub-network within a large-scale supernet before converging to the best sub-network architecture. Thereby, how to map these iterative network inference tasks onto the CIM arrays makes a big difference in system performance. To realize efficient in-memory supernet sampling and evaluation, we design a novel mapping method that tactically executes a group of sub-nets in the CIM arrays, not only to boost the sub-net concurrency but also to eliminate the repetitive operations shared by these subnets. Meanwhile, to further enhance the subnet-level operation concurrency and sharing in the CIM arrays, we also tailor a novel CIM-friendly one-shot NAS algorithm that purposely samples those operation-sharing subnets in each iteration while still maintaining the convergence performance of NAS. According to the experimental results, our CIM NAS accelerator achieves an improvement of 196.6× and 1200× in performance speedup and energy saving respectively compared to the CPU+GPU baseline.