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Network-on-Chip (NoC) plays a significant role in the performance of a DNNaccelerator. The scalability and modular design property of the NoC help inimproving the performance of a DNN execution by providing flexibility inrunning different kinds of workloads. Data movement in a DNN workload is stilla challenging task for DNN accelerators and hence a novel approach is required.In this paper, we propose the In-Network Accumulation (INA) method to furtheraccelerate a DNN workload execution on a many-core spatial DNN accelerator forthe Weight Stationary (WS) dataflow model. The INA method expands the router'sfunction to support partial sum accumulation. This method avoids the overheadof injecting and ejecting an incoming partial sum to the local processingelement. The simulation results on AlexNet, ResNet-50, and VGG-16 workloadsshow that the proposed INA method achieves 1.22x improvement in latency and2.16x improvement in power consumption on the WS dataflow model.

In-Network Accumulation: Extending the Role of NoC for DNN Acceleration