NeuroXplorer 1.0: An Extensible Framework for Architectural Exploration with Spiking Neural Networks

TL;DR

NeuroXplorer 1.0 is a versatile, extensible simulation framework designed for detailed architectural exploration of neuromorphic systems and SNN hardware-software co-design, supporting both low-level and high-level analyses.

Contribution

It introduces NeuroXplorer, a flexible simulation tool that models neuromorphic architectures with customizable details and supports multi-metric optimization for SNN hardware design.

Findings

Enables detailed architectural exploration of neuromorphic systems.

Supports optimization based on energy, throughput, latency, and SNN-specific metrics.

Demonstrates effectiveness through case studies with state-of-the-art machine learning models.

Abstract

Recently, both industry and academia have proposed many different neuromorphic architectures to execute applications that are designed with Spiking Neural Network (SNN). Consequently, there is a growing need for an extensible simulation framework that can perform architectural explorations with SNNs, including both platform-based design of today's hardware, and hardware-software co-design and design-technology co-optimization of the future. We present NeuroXplorer, a fast and extensible framework that is based on a generalized template for modeling a neuromorphic architecture that can be infused with the specific details of a given hardware and/or technology. NeuroXplorer can perform both low-level cycle-accurate architectural simulations and high-level analysis with data-flow abstractions. NeuroXplorer's optimization engine can incorporate hardware-oriented metrics such as energy, throughput, and latency, as well as SNN-oriented metrics such as inter-spike interval distortion and spike disorder, which directly impact SNN performance. We demonstrate the architectural exploration capabilities of NeuroXplorer through case studies with many state-of-the-art machine learning models.