BiNMAC: Binarized neural Network Manycore ACcelerator

This paper presents a low power, domain-specific manycore accelerator referred to as "BiNMAC"- Binarized neural Network Manycore ACcelerator, which effectively maps and executes Binary Deep Neural Networks (BNNs). With only 2.40% and 1.88% area and power overhead, novel instructions such as Population-Count and Patch-Select are added to the ISA of the BiNMAC, each of which replaces frequently used functions that would have taken 52 and 4 clock cycles respectively with 1 clock cycle. A 64-cluster architecture of the BiNMAC is fully placed and routed in 65~nm TSMC CMOS technology, where a single cluster occupies an area of 0.53 mm^2 with a power of 223 mW at 1 GHz clock frequency. The 64-cluster architecture takes 36.5 mm^2 area and, if fully utilized, consumes a power of 16.4 W. We also propose a multilayer perceptron (MLP) neural network for multimodal time-series data classification. Binarized versions of the 3-layers MLP and ResNet-20 are implemented on BiNMAC. The implementation results show that BiNMAC consumes 0.02 mJ and 3.8 mJ energy which is 13 times and 30 times lower than the implementation of standard non-binarized MLP and ResNet-20 on an equivalent predecessor platform. To compare the performance of the BiNMAC with other off-the-shelf platforms, the two networks are also implemented on the NVIDIA Jetson TX2 SoC (CPU+GPU). BiNMAC achieves 22 times and 78 times higher throughput and 23 times and 41 times lower energy consumption compared to TX2 SoC for the binarized MLP and ResNet-20, respectively.