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The ever-increasing computation complexity of fast-growing Deep NeuralNetworks (DNNs) has requested new computing paradigms to overcome the memorywall in conventional Von Neumann computing architectures. The emergingComputing-In-Memory (CIM) architecture has been a promising candidate toaccelerate neural network computing. However, the data movement between CIMarrays may still dominate the total power consumption in conventional designs.This paper proposes a flexible CIM processor architecture named Domino toenable stream computing and local data access to significantly reduce the datamovement energy. Meanwhile, Domino employs tailored distributed instructionscheduling within Network-on-Chip (NoC) to implement inter-memory-computing andattain mapping flexibility. The evaluation with prevailing CNN models showsthat Domino achieves 1.15-to-9.49$\times$ power efficiency over severalstate-of-the-art CIM accelerators and improves the throughput by1.57-to-12.96$\times$.

Domino: A Tailored Network-on-Chip Architecture to Enable Highly Localized Inter- and Intra-Memory DNN Computing