Toward End-to-End Event-Driven Systems: A Hardware-Oriented Hierarchical Spiking Predictive Coding Framework for On-Device Learning

Abstract

Integrating on-device learning into autonomous systems requires neural network frameworks that achieve both high energy efficiency and low latency. While spiking neural networks (SNNs) provide a promising event-driven paradigm, implementing hardware-efficient learning remains a challenge due to the computational overhead of error signaling and global gradients. This paper introduces a hardware-oriented hierarchical spiking predictive coding (SPC) framework designed for end-to-end event-driven systems. The proposed architecture implements an implicit prediction error encoding mechanism through local lateral and supervisory feedback connections, eliminating the need for dedicated error-storage memory or complex inter-layer error communication. The entire framework is structured and parameterized for physical implementation, utilizing digital-aligned simulations and arithmetic operations. We evaluate the system on neuromorphic datasets using a fixed 1 ms temporal resolution to mirror real-time hardware constraints. Experimental results demonstrate that the SPC framework can effectively identify stimuli from transient event streams, achieving stable on-device learning. Our work provides a practical path toward deploying low-power, scalable hierarchical spiking networks in resource-constrained environments.