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

TL;DR

This paper presents a co-designed algorithm-hardware framework for neuromorphic networks with dual memory pathways, inspired by the brain, achieving high accuracy, efficiency, and scalability for long-sequence tasks.

Contribution

It introduces a dual memory pathway architecture inspired by cortical organization, combining algorithmic stability with hardware efficiency in neuromorphic systems.

Findings

Achieves 40-60% fewer parameters than state-of-the-art SNNs.

Demonstrates over 4X increase in throughput.

Over 5X improvement in energy efficiency.

Abstract

Spiking neural networks excel at event-driven sensing. Yet, maintaining task-relevant context over long timescales both algorithmically and in hardware, while respecting both tight energy and memory budgets, remains a core challenge in the field. We address this challenge through an algorithm-hardware co-design effort. At the algorithm level, inspired by the cortical fast-slow organization in the brain, we introduce a neural network with an explicit slow memory pathway that, combined with fast spiking activity, enables a dual memory pathway (DMP) architecture in which each layer maintains a compact low-dimensional state that summarizes recent activity and modulates spiking dynamics. This explicit memory 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. At the hardware level, we introduce a near-memory-compute architecture that fully leverages the advantages of the DMP architecture by retaining its compact shared state while optimizing dataflow, across heterogeneous sparse-spike and dense-memory pathways. We show experimental results that demonstrate more than a 4X increase in throughput and over a 5X improvement in energy efficiency compared with state-of-the-art implementations. Together, these contributions demonstrate that biological principles can guide functional abstractions that are both algorithmically effective and hardware-efficient, establishing a scalable co-design framework for real-time neuromorphic computation and learning.