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 via a uniform per-neuron clamping primitive that enforces boundary conditions while leaving the internal update schedule unchanged. The design is a deterministic, synthesizable RTL substrate built around a sequential MAC datapath and a fixed finite-state schedule. Rather than executing a task-specific instruction sequence inside the learning substrate, the system evolves under fixed local update rules, with task structure imposed through connectivity, parameters, and boundary conditions. The contribution of this work is not a new learning rule, but a complete synthesizable digital substrate that executes predictive-coding learning dynamics directly in hardware.

翻译：反向传播（Backpropagation）虽推动了现代深度学习的发展，但由于其依赖全局误差传播、相位分离和集中式存储器，难以实现为在线、全分布式硬件学习系统。预测编码提供了一种替代方案，其中推理和学习源自相邻层之间的局部预测误差动力学。本文提出一种数字架构，直接在硬件中实现离散时间预测编码更新。每个神经核维持自身的活动状态、预测误差与突触权重，仅通过硬连线连接与相邻层通信。通过统一的逐神经元钳位原语支持监督学习与推理——该原语在保持内部更新调度不变的前提下施加边界条件。该设计是一个确定性的可综合RTL基底，构建于顺序型MAC数据路径和固定有限状态调度之上。学习基底内不执行特定任务的指令序列，系统在固定的局部更新规则下演化，而任务结构通过连接性、参数及边界条件施加。本工作的贡献并非提出新学习规则，而是提供一个完整可综合的数字基底，直接在硬件中执行预测编码学习动力学。