WiNN: Wireless Interconnect based Neural Network Accelerator

Deep Neural Networks (DNNs) have demonstrated promising performance in accuracy for several applications such as image processing, speech recognition, and autonomous systems and vehicles. Spatial accelerators have been proposed to achieve high parallelism with arrays of processing elements (PE) and energy efficient data movement using traditional Network-on-Chip (NoC) architectures. However, larger DNN models impose high bandwidth and low latency communication demands between PEs, which is a fundamental challenge for metallic NoC architectures. In this paper, we propose WiNN, a wireless and wired interconnected neural network accelerator that employs on-chip wireless links to provide high network bandwidth and single cycle multicast communication. We design separate wireless networks modulated with two different frequency bands one each for the weights and input Highly directional antennas are implemented to avoid noise and interference. We propose multicast-for-wireless (MW) dataflow for our proposed accelerator that efficiently exploits the wireless channels’ multicast capabilities to reduce the communication overheads. Our novel wireless transmitter integrates on-off keying (OOK) modulator with power amplifier that results in significant energy savings. Our simulation results show that WiNN achieves 74% latency reduction and 37.5% energy saving when compared to state-of-art metallic link-based accelerators, 38.1% latency reduction and 19.4% energy saving when compared to prior wireless accelerators for various neural networks (AlexNet, VGG16, and ResNet-50).