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.

翻译：脉冲神经网络（SNNs）已展现出解决多样化机器学习任务的潜力，并能实现超低功耗/能耗计算。为进一步提升SNN推理的性能与效率，研究采用了基于新兴器件技术（如阻变随机存取存储器）的存内计算（CIM）范式。然而，现有大多数SNN架构的设计未充分考虑应用场景及底层CIM硬件的约束条件（如存储容量、面积、延迟和能耗）。此外，多数SNN设计衍生自人工神经网络，其网络运算机制与SNNs存在本质差异。这些局限阻碍了SNNs在精度与能效方面发挥其全部潜力。为此，我们提出HASNAS——一种面向神经形态CIM系统的新型硬件感知神经架构搜索（NAS）框架，该框架能够在给定存储容量、面积、延迟及能耗约束下，搜索出具有高精度的SNN。为实现这一目标，HASNAS采用以下关键步骤：（1）优化SNN运算以实现高精度；（2）构建便于高效学习过程的SNN架构；（3）设计系统化的硬件感知搜索算法以满足约束条件。实验结果表明，相较于现有最优方案，我们的HASNAS能以最高11倍的加速比快速搜索出保持高精度的SNN，并满足以下约束条件：在CIFAR10和CIFAR100数据集上实现4×10^6参数存储量、100mm^2面积、400ms延迟及120uJ能耗；而现有最优方案无法同时满足这些约束。通过这种方式，我们的HASNAS能够实现高效的设计自动化，为多样化应用提供高性能、高能效的神经形态存内计算系统。