NeuroNAS: A Framework for Energy-Efficient Neuromorphic Compute-in-Memory Systems using Hardware-Aware Spiking Neural Architecture Search

Spiking Neural Networks (SNNs) have demonstrated capabilities for solving diverse machine learning tasks with ultra-low power/energy consumption. To maximize the performance and efficiency of SNN inference, the Compute-in-Memory (CIM) hardware accelerators with emerging device technologies (e.g., RRAM) have been employed. However, SNN architectures are typically developed without considering constraints from the application and the underlying CIM hardware, thereby hindering SNNs from reaching their full potential in accuracy and efficiency. To address this, we propose NeuroNAS, a novel framework for developing energy-efficient neuromorphic CIM systems using a hardware-aware spiking neural architecture search (NAS), i.e., by quickly finding an SNN architecture that offers high accuracy under the given constraints (e.g., memory, area, latency, and energy consumption). NeuroNAS employs the following key steps: (1) optimizing SNN operations to enable efficient NAS, (2) employing quantization to minimize the memory footprint, (3) developing an SNN architecture that facilitates an effective learning, and (4) devising a systematic hardware-aware search algorithm to meet the constraints. Compared to the state-of-the-art, NeuroNAS with 8bit weight precision quickly finds SNNs that maintain high accuracy by up to 6.6x search time speed-ups, while achieving up to 92% area savings, 1.2x latency speed-ups, 84% energy savings across CIFAR-10, CIFAR-100, and TinyImageNet-200 datasets; while the state-of-the-art fail to meet all constraints at once. In this manner, NeuroNAS enables efficient design automation in developing energy-efficient neuromorphic CIM systems for diverse ML-based applications.