To address the 'memory wall' problem in NN hardware acceleration, we introduce HALO-CAT, a software-hardware co-design optimized for Hidden Neural Network (HNN) processing. HALO-CAT integrates Layer-Penetrative Tiling (LPT) for algorithmic efficiency, reducing intermediate result sizes. Furthermore, the architecture employs an activation-localized computing-in-memory approach to minimize data movement. This design significantly enhances energy efficiency, achieving a 14.2x reduction in activation memory capacity and a 17.8x decrease in energy consumption, with only a 1.5% loss in accuracy, compared to traditional HNN processors.

翻译：为解决神经网络硬件加速中的“存储墙”问题，我们提出了HALO-CAT——一种面向隐藏神经网络（HNN）处理优化的软硬件协同设计。HALO-CAT通过集成层级穿透平铺（LPT）提升算法效率，从而减小中间结果尺寸。此外，该架构采用激活局部化存内计算方法以最小化数据移动。该设计显著提升了能效，与传统HNN处理器相比，激活存储容量降低14.2倍，能耗降低17.8倍，而精度损失仅为1.5%。