Mixed-signal implementation of feedback-control optimizer for single-layer Spiking Neural Networks

2026-03-25

AI Summary

This work addresses the lack of efficient and scalable on-chip training capabilities in existing neuromorphic chips, which hinders autonomous adaptive learning. The authors propose a feedback control–based optimizer and demonstrate its first hardware implementation on a real mixed-signal neuromorphic processor, validating its feasibility in physical systems. Through algorithm–hardware co-design and an In-The-Loop training framework, the system enables online learning for single-layer spiking neural networks. Evaluated on binary classification and the nonlinear Yin-Yang task, the approach achieves performance comparable to numerical simulations and gradient-based baselines, thereby demonstrating its effectiveness and practicality for on-device neuromorphic learning.

Technology Category

• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Social networks and social learning
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

On-chip learning is key to scalable and adaptive neuromorphic systems, yet existing training methods are either difficult to implement in hardware or overly restrictive. However, recent studies show that feedback-control optimizers can enable expressive, on-chip training of neuromorphic devices. In this work, we present a proof-of-concept implementation of such feedback-control optimizers on a mixed-signal neuromorphic processor. We assess the proposed approach in an In-The-Loop(ITL) training setup on both a binary classification task and the nonlinear Yin-Yang problem, demonstrating on-chip training that matches the performance of numerical simulations and gradient-based baselines. Our results highlight the feasibility of feedback-driven, online learning under realistic mixed-signal constraints, and represent a co-design approach toward embedding such rules directly in silicon for autonomous and adaptive neuromorphic computing.

Problem

• on-chip learning
• spiking neural networks
• mixed-signal neuromorphic hardware
• feedback-control optimizer
• neuromorphic computing

Innovation

• feedback-control optimizer
• on-chip learning
• mixed-signal neuromorphic processor
• spiking neural networks
• hardware-software co-design