Algorithm-hardware co-design of neuromorphic networks with dual memory pathways

2026-05-05 · Source: cs.NE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Expert, medium

Summary

Researchers have developed a novel algorithm-hardware co-design for neuromorphic networks, introducing a Dual Memory Pathway (DMP) architecture inspired by the brain's cortical fast-slow organization. This architecture incorporates an explicit slow memory pathway that, combined with fast spiking activity, allows each layer to maintain a compact, low-dimensional state summarizing recent activity and modulating spiking dynamics. This design stabilizes learning while preserving event-driven sparsity, achieving competitive accuracy on long-sequence benchmarks with 40-60% fewer parameters than equivalent state-of-the-art spiking neural networks. Concurrently, a near-memory-compute hardware architecture was developed to leverage DMP's advantages, optimizing dataflow across heterogeneous sparse-spike and dense-memory pathways. Experimental results demonstrate over a 4x increase in throughput and more than a 5x improvement in energy efficiency compared to existing implementations.

Key takeaway

For research scientists developing neuromorphic systems, this co-design approach offers a path to overcome the trade-off between temporal memory and hardware efficiency. You should consider integrating explicit, low-dimensional memory pathways into your SNN designs and explore near-memory-compute architectures to achieve superior performance with reduced parameter counts and significantly improved energy efficiency, especially for long-sequence tasks.

Key insights

A dual memory pathway architecture and co-designed hardware significantly enhance neuromorphic network efficiency and temporal processing.

Principles

• Cortical fast-slow organization improves temporal context.
• Explicit low-dimensional memory stabilizes SNN learning.
• Algorithm-hardware co-design optimizes performance.

Method

The DMP-SNN uses a low-dimensional state vector (d « N) per layer to capture slow contextual dynamics, feeding back as input current. This is paired with a near-memory-compute architecture for heterogeneous dataflow.

In practice

• Achieves 97.3% on PS-MNIST and 99.3% on S-MNIST.
• Reduces parameters by 40-60% compared to SOTA SNNs.
• Increases throughput by >4x and energy efficiency by >5x.

Topics

• Neuromorphic Networks
• Spiking Neural Networks
• Dual Memory Pathway Architecture
• Algorithm-Hardware Co-design
• Near-Memory-Compute

Best for: Research Scientist, AI Scientist, AI Hardware Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.NE updates on arXiv.org.