  Spiking Neural Networks (SNNs) have shown capabilities for solving diverse
machine learning tasks with ultra-low-power/energy computation. To further
improve the performance and efficiency of SNN inference, the Compute-in-Memory
(CIM) paradigm with emerging device technologies such as resistive random
access memory is employed. However, most of SNN architectures are developed
without considering constraints from the application and the underlying CIM
hardware (e.g., memory, area, latency, and energy consumption). Moreover, most
of SNN designs are derived from the Artificial Neural Networks, whose network
operations are different from SNNs. These limitations hinder SNNs from reaching
their full potential in accuracy and efficiency. Toward this, we propose
HASNAS, a novel hardware-aware spiking neural architecture search (NAS)
framework for neuromorphic CIM systems that finds an SNN that offers high
accuracy under the given memory, area, latency, and energy constraints. To
achieve this, HASNAS employs the following key steps: (1) optimizing SNN
operations to achieve high accuracy, (2) developing an SNN architecture that
facilitates an effective learning process, and (3) devising a systematic
hardware-aware search algorithm to meet the constraints. The experimental
results show that our HASNAS quickly finds an SNN that maintains high accuracy
compared to the state-of-the-art by up to 11x speed-up, and meets the given
constraints: 4x10^6 parameters of memory, 100mm^2 of area, 400ms of latency,
and 120uJ energy consumption for CIFAR10 and CIFAR100; while the
state-of-the-art fails to meet the constraints. In this manner, our HASNAS can
enable efficient design automation for providing high-performance and
energy-efficient neuromorphic CIM systems for diverse applications.
