Journal of Computers, Mechanical and Management

Rights and permissions

© The Author(s) 2025.

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits the non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as appropriate credit to the original author(s) and the source is given by providing a link to the Creative Commons License and changes need to be indicated if there are any. The images or other third-party material in this article are included in the article's Creative Commons License, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons License and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this License, visit: httpshttps://creativecommons.org/licenses/by-nc/4.0/

G. D. Jadhav, R. V. Dagade, S. Jakhade, K. Jadhav, R. Hinge, S. Joshi, The unified neuromorphic assembly layer for hardware-agnostic compilation in neuromorphic computing, Journal of Smart Sensors and Computing, 2025, 1(3), 25212, doi: https://doi.org/10.64189/ssc.25212.

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.