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：反向传播实现了现代深度学习，但由于全局误差传播、相位分离以及对集中式内存的严重依赖，难以实现为在线、全分布的硬件学习系统。预测编码提供了一种替代方案，其中推理和学习源于相邻层之间的局部预测误差动态。本文提出了一种数字架构，直接在硬件中实现离散时间预测编码更新。每个神经核心维护自身的活动、预测误差和突触权重，并仅通过硬连线连接与相邻层通信。通过统一的每神经元钳制原语支持监督学习和推理，该原语强制执行边界条件，同时保持内部更新调度不变。该设计是一个确定性的、可综合的RTL基板，构建在顺序MAC数据路径和固定的有限状态调度之上。系统不在学习基板内执行特定于任务的指令序列，而是在固定的局部更新规则下演化，任务结构通过连接、参数和边界条件施加。本工作的贡献并非提出新的学习规则，而是提供一个完整的可综合数字基板，直接在硬件中执行预测编码学习动态。