MMNNN: A tree-based Multicast Mechanism for NoC-based deep Neural Network accelerators

Abstract

Network-on-Chip (NoC) devices have been widely used in multiprocessor systems. In recent years, NoC-based Deep Neural Network (DNN) accelerators have been proposed to connect neural computing devices using NoCs. Such designs dramatically reduce off-chip memory accesses of these platforms. However, the large number of one-to-many packet transfers significantly degrade performance with traditional unicast channels. We propose a multicast mechanism for a NoC-based DNN accelerator called Multicast Mechanism for NoC-based Neural Network accelerator (MMNNN). To do so, we propose a tree-based multicast routing algorithm with excellent scalability and the ability to minimize the number of packets in the network. We also propose a router architecture for single-flit packets. Our proposed router transfers flits to multiple destinations in a single process and has no head-of-line blocking issue, offering higher throughput and lower latency than traditional wormhole router architectures. Simulation results show that our proposed multicast mechanism offers excellent performance in classification latency, average packet latency, and energy consumption.