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The increasing popularity of deep neural network (DNN) applications demandshigh computing power and efficient hardware accelerator architecture. DNNaccelerators use a large number of processing elements (PEs) and on-chip memoryfor storing weights and other parameters. As the communication backbone of aDNN accelerator, networks-on-chip (NoC) play an important role in supportingvarious dataflow patterns and enabling processing with communicationparallelism in a DNN accelerator. However, the widely used mesh-based NoCarchitectures inherently cannot support the efficient one-to-many andmany-to-one traffic largely existing in DNN workloads. In this paper, wepropose a modified mesh architecture with a one-way/two-way streaming bus tospeedup one-to-many (multicast) traffic, and the use of gather packets tosupport many-to-one (gather) traffic. The analysis of the runtime latency of aconvolutional layer shows that the two-way streaming architecture achievesbetter improvement than the one-way streaming architecture for an OutputStationary (OS) dataflow architecture. The simulation results demonstrate thatthe gather packets can help to reduce the runtime latency up to 1.8 times andnetwork power consumption up to 1.7 times, compared with the repetitive unicastmethod on modified mesh architectures supporting two-way streaming.

Data Streaming and Traffic Gathering in Mesh-based NoC for Deep Neural Network Acceleration