A Synthesizable RTL Implementation of Predictive Coding Networks

2026-03-20 · Source: cs.NE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, AI Hardware Architecture · Depth: Expert, extended

Summary

A new digital architecture implements a discrete-time predictive coding update directly in hardware, offering an alternative to backpropagation for online, fully distributed learning systems. This synthesizable RTL substrate, available open-source on GitHub, features neural cores that manage their own activity, prediction error, and synaptic weights, communicating only with adjacent layers. It supports supervised learning and inference through a uniform per-neuron clamping mechanism, enforcing boundary conditions while maintaining a fixed internal update schedule. The design utilizes a sequential Multiply-Accumulate (MAC) datapath and a fixed finite-state schedule, where task structure is defined by connectivity, parameters, and boundary conditions rather than task-specific instruction sequences. Experiments on various network sizes (e.g., 2→4→3 ReLU, 2→2→1 tanh) demonstrate rapid initial MSE descent followed by stable residual floors, confirming the architecture's ability to generalize across scales without RTL modifications.

Key takeaway

For AI Scientists developing embedded learning systems, this synthesizable predictive coding hardware offers a compelling alternative to traditional backpropagation. Its local update rules and fixed FSM schedule simplify distributed hardware implementation, potentially enabling more energy-efficient and online adaptive systems. You should explore this open-source RTL implementation to evaluate its suitability for applications requiring on-chip learning without global coordination or centralized memory, particularly for tasks expressible as inference under constraints.

Key insights

Predictive coding offers a local, hardware-friendly alternative to backpropagation for distributed online learning.

Principles

• Local dynamics enable distributed learning.
• Fixed update rules support diverse tasks.
• Hardware-software co-design is crucial.

Method

The architecture uses a neural core with a sequential MAC datapath and a fixed FSM schedule (PRED→ERR→BACKSUM→BACKVEC→WUP→STATE) for tick-based, discrete-time predictive coding updates, with clamping for supervised learning.

In practice

• Implement local learning with predictive coding.
• Use hardwired connections for inter-layer communication.
• Leverage clamping for supervised training.

Topics

• Predictive Coding Networks
• Hardware Acceleration
• RTL Design
• Distributed Learning
• Neuromorphic Computing

Code references

• alskaf1293/neuralcomputer

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

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