a-low-precision-simd-spiking-neural-compute-engine

---
name: a-low-precision-simd-spiking-neural-compute-engine
description: "Spiking Neural Networks (SNNs) offer a promising solution for energy-efficient edge intelligence; however, their hardware deployment is constrained by memory overhead, inefficient scaling operations, ..."
version: 1.0.0
author: Research Synthesis
license: MIT
metadata:
  hermes:
    tags: [neuroscience, research, arxiv]
    source_paper: "L-SPINE: A Low-Precision SIMD Spiking Neural Compute Engine for Resource-efficient Edge Inference (arXiv:2604.03626v1)"
    published: "2026-04-04"
    relevance_score: 14
---

# L-SPINE: A Low-Precision SIMD Spiking Neural Compute Engine for Resource-efficient Edge Inference

## Overview
Spiking Neural Networks (SNNs) offer a promising solution for energy-efficient edge intelligence; however, their hardware deployment is constrained by memory overhead, inefficient scaling operations, and limited parallelism. This work proposes L-SPINE, a low-precision SIMD-enabled spiking neural compute engine for efficient edge inference. The architecture features a unified multi-precision datapath supporting 2-bit, 4-bit, and 8-bit operations, leveraging a multiplier-less shift-add model for neuron dynamics and synaptic accumulation. Implemented on an AMD VC707 FPGA, the proposed neuron requires only 459 LUTs and 408 FFs, achieving a critical delay of 0.39 ns and 4.2 mW power. At the system level, L-SPINE achieves 46.37K LUTs, 30.4K FFs, 2.38 ms latency, and 0.54 W power. Compared to CPU and GPU platforms, it reduces inference latency from seconds to milliseconds, achieving an up to three orders-of-magnitude improvement in energy efficiency. Quantisation analysis shows that INT2/INT4 configurations significantly reduce memory footprint with minimal accuracy loss. These results establish L-SPINE as a scalable and efficient solution for real-time edge SNN deployment.

## Authors
Sonu Kumar, Mukul Lokhande, Santosh Kumar Vishvakarma

## Publication Information
- **arXiv ID**: 2604.03626v1
- **Published**: 2026-04-04
- **Category**: spiking neural network

## Key Insights
- Research focuses on advancing understanding in spiking neural network
- Relevance score: 14/20

## Links
- arXiv Abstract: https://arxiv.org/abs/2604.03626v1
- arXiv PDF: https://arxiv.org/pdf/2604.03626v1

## References
Sonu Kumar, Mukul Lokhande, Santosh Kumar Vishvakarma. "L-SPINE: A Low-Precision SIMD Spiking Neural Compute Engine for Resource-efficient Edge Inference". arXiv:2604.03626v1, 2026-04-04.