A New Dataflow Implementation to Improve Energy Efficiency of Monolithic 3D Systolic Arrays

Systolic arrays are popular for executing deep neural networks (DNNs) at the edge. Low latency and energy efficiency are key requirements in edge devices such as drones and autonomous vehicles. Monolithic 3D (MONO3D) is an emerging 3D integration technique that offers ultra-high bandwidth among processing and memory elements with a negligible area overhead. Such high bandwidth can help meet the ever-growing latency and energy efficiency demands for DNNs. This paper presents a novel implementation for weight stationary (WS) dataflow in MONO3D systolic arrays, called WS-MONO3D. WS-MONO3D utilizes multiple resistive RAM layers and SRAM with high-density vertical interconnects to multicast inputs and perform high-bandwidth weight pre-loading while maintaining the same order of multiply-and-accumulate operations as in native WS dataflow. Consequently, WS-MONO3D eliminates input and weight forwarding cycles and, thus, provides up to 40 native WS implementation in 2D at iso-configuration. WS-MONO3D also provides 10X improvement in inference per second per watt per footprint due to multiple vertical tiers. Finally, we also show that temperature impacts the energy efficiency benefits in WS-MONO3D.