SPINDLE: SPINtronic Deep Learning Engine for Large-scale Neuromorphic Computing

Deep Learning Networks (DLNs) are bio-inspired large-scale neural networks that are widely used in emerging vision, analytics, and search applications. The high computation and storage requirements of DLNs have led to the exploration of various avenues for their efficient realization. Concurrently, the ability of emerging post-CMOS devices to efficiently mimic neurons and synapses has led to great interest in their use for neuromorphic computing. We describe SPINDLE, a programmable processor for deep learning based on spintronic devices. SPINDLE exploits the unique ability of spintronic devices to realize highly dense and energy-efficient neurons and memory, which form the fundamental building blocks of DLNs. SPINDLE consists of a three-tier hierarchy of processing elements to capture the nested parallelism present in DLNs, and a two-level memory hierarchy to facilitate data reuse. It can be programmed to execute DLNs with widely varying topologies for different applications. SPINDLE employs techniques to limit the overheads of spin-to-charge conversion, and utilizes output and weight quantization to enhance the efficiency of spin-neurons. We evaluate SPINDLE using a device-to-architecture modeling framework and a set of widely used DLN applications (handwriting recognition, face detection, and object recognition). Our results indicate that SPINDLE achieves 14.4X reduction in energy consumption and 20.4X reduction in EDP over the CMOS baseline under iso-area conditions.