MUGNoC: A Software-configured Multicast-Unicast-Gather NoC for Accelerating CNN Dataflows

Current communication infrastructures for convolutional neural networks (CNNs) only focus on specific transmission patterns, not applicable to benefit the whole system if the dataflow changes or different dataflows run in one system. To reduce data movement, various CNN dataflows are presented. For these dataflows, param-eters and results are delivered using different traffic patterns, i.e., multicast, unicast, and gather, preventing dataflow-specific commu-nication backbones from benefiting the entire system if the dataflow changes or different dataflows run in the same system. Thus, in this paper, we propose MUG-NoC to support typical traffic patterns and accelerate them, therefore boosting multiple dataflows. Specifically, (i) we for the first time support multicast in 2D-mesh software configurable NoC by revising router configuration and proposing the efficient multicast routing; (ii) we decrease unicast latency by transmitting data through the different routes in parallel; (iii) we reduce output gather overheads by pipelining basic dataflow units. Experiments show that at least our proposed design can reduce 39.2% total data transmission time compared with the state-of-the-art CNN communication backbone.