Abstract

The programming of neuromorphic assembly has advanced steadily, providing essential tools and paradigms to help connect the gap between abstract Spiking Neural Network (SNN) models and brain-inspired computing hardware. This work presents UNAL (Unified Adaptive, Hardware-Agnostic Neuromorphic Assembly Layer). This compilation framework translates high-level Spiking Neural Network (SNN) models into portable, spike-level assembly across heterogeneous neuromorphic platforms. UNAL introduces a unified intermediate representation (UNAL-IR), a compact instruction set, and an optimization-driven mapping pipeline that jointly addresses latency, energy efficiency, routing congestion, and adaptability. Quantitative evaluation on standard SNN benchmarks (DVS Gesture and CIFAR-10 SNN) mapped to Intel Loihi 2 demonstrates 18–32% latency reduction, 21–38% energy savings, and 25–40% lower routing congestion compared to Loihi-native and platform-specific tool chains. A smart-city surveillance case study further validates the deployment of real-time edge computing. These results establish UNAL as a scalable and future-ready neuromorphic compiler infrastructure.

Graphical Abstract

Novelty Statement

This work introduces a hardware-agnostic neuromorphic compilation approach that unifies heterogeneous SNN execution models, supports adaptive neural dynamics, and automates spike-level instruction synthesis.