NeuroComp: A Cross-Platform Compiler for Efficient SNN Deployment on Neuromorphic Hardware

Abstract

Spiking Neural Networks (SNNs) offer substantial energy efficiency advantages, yet deploying them across diverse neuromorphic hardware remains challenging. Existing toolchains exhibit a critical trade-off: platform-specific frameworks lack portability, while low-level programming interfaces demand extensive hardware expertise and produce non-portable code. Furthermore, conventional deep learning compilers fail to handle the unique temporal dynamics of SNNs and the idiosyncratic constraints of neuromorphic architectures. To bridge this gap, we present NeuroComp, a cross-platform compiler that automatically generates functionally correct and high-performance code for heterogeneous neuromorphic backends. Our core insight is that the diverse execution behaviors of neuromorphic hardware can be universally modeled as parallelizable scalar computations expressible via nested loops. Building on this, NeuroComp introduces a unified multi-level loop abstraction to systematically align SNN computational semantics with hardware capacities. To efficiently navigate the vast implementation space, we propose a decoupled two-stage scheduling strategy. The first stage performs batch partitioning to guarantee functional correctness and strict architectural compliance, while the second stage optimizes inter-batch execution orders for maximum data reuse, guided by lightweight, hardware-specific cost models and heuristic pruning. We evaluate NeuroComp on three representative architectural paradigms: an instruction-extended CPU, a dedicated accelerator, and a many-core hardware. Experimental results demonstrate that NeuroComp consistently achieves competitive performance compared to meticulously hand-optimized implementations and significantly outperforms existing generic automated compilers, effectively minimizing development effort while unlocking cross-platform portability.