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To address the 'memory wall' problem in NN hardware acceleration, weintroduce HALO-CAT, a software-hardware co-design optimized for Hidden NeuralNetwork (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 approachto minimize data movement. This design significantly enhances energyefficiency, achieving a 14.2x reduction in activation memory capacity and a17.8x decrease in energy consumption, with only a 1.5% loss in accuracy,compared to traditional HNN processors.

HALO-CAT: A Hidden Network Processor with Activation-Localized CIM Architecture and Layer-Penetrative Tiling