A Synthesizable RTL Implementation of Predictive Coding Networks
Backpropagation has enabled modern deep learning but is difficult to realize as an online, fully distributed hardware learning system due to global error propagation, phase separation, and heavy reliance on centralized memory. Predictive coding offers an alternative in which inference and learning arise from local prediction-error dynamics between adjacent layers. This paper presents a digital architecture that implements a discrete-time predictive coding update directly in hardware. Each neural core maintains its own activity, prediction error, and synaptic weights, and communicates only with adjacent layers through hardwired connections. Supervised learning and inference are supported v...
| Authors | Timothy Oh |
|---|---|
| Primary category | cs.NE |
| Published | 18 March 2026 |
| Last updated at source | 2 May 2026 |
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