CANDLES: Channel-Aware Novel Dataflow-Microarchitecture Co-Design for Low Energy Sparse Neural Network Acceleration

Several deep neural network (DNN) accelerators have been designed to exploit the sparsity exhibited by DNN activations and weights. State-of-the-art sparse accelerators can be described as either Pixel-first or Channel-first accelerators, each with its unique dataflow and compression format aiding its dataflow. The former expends significant energy updating neuron partial sums, while the latter expends significant energy in handling the index metadata. This work introduces a novel microarchitecture and dataflow that reconciles these trade-offs by adopting a Pixel-first compression and Channel-first dataflow. The proposed microarchitecture has a simpler index-generation logic combined with an accumulator buffer hierarchy and crossbar with low wiring overhead. The compression format and dataflow promote high temporal locality in neuron updates, further lowering energy. Finally, we introduce work partitions across processing elements that naturally lead to load balance without offline analysis. Compared to four state-of-the-art baselines, the proposed architecture, CANDLES, significantly outperforms three and matches the performance of the fourth. In terms of energy, CANDLES is between 2.5× and 5.6× more energy-efficient than these four baselines.