Spiking neural networks (SNNs) offer a promising biologically-plausible computing model and lend themselves to ultra-low-power event-driven processing on neuromorphic processors. Compared with the conventional artificial neural networks, SNNs are well-suited for processing complex spatiotemporal data.In this dissertation, we aim to address key difficulties in accelerating SNNs: developing bio-plausible and hardware friendly algorithm, efficient processing of the added temporal dimension, and handling unstructured sparsity emergent in both space and time.

First, training SNNs to reach the same performancesof conventional deep artificial neural networks (ANNs), particularly with error backpropagation (BP) algorithms, poses a significant challenge due to inherent complex dynamics and non-differentiable spike activities of spiking neurons. In this dissertation, we present the first study on realizing competitive spike-train level backpropagation (BP) like algorithms to enable on-chip training of SNNs. We propose a novel spike-train level direct feedback alignment (ST-DFA) algorithm, which is much more bio-plausible and hardware friendly than BP. Algorithm and hardware co-optimization and efficient online neural signal computation are explored for on-chip implementation of ST-DFA. On the Xilinx ZC706 FPGA board, the proposed hardware-efficient ST-DFA shows excellent performance vs. overhead tradeoffs for real-world speech and image classification applications.

Despite its significance, dataflow optimization of spiking neural accelerator architectures has not been extensively studied. Recognizing the need for efficient processing of complex spatiotemporal data while considering the all-or-none nature of spiking activities, we propose holistic reconfigurable dataflow optimization for systolic array acceleration of spiking convolutional networks (S-CNNs). A novel scheme is introduced for parallel acceleration of computation across multiple time points, which further allows for systemic optimization of variable tiling for a large performance and efficiency gains. Also, we pack multiple time points into a single time window (TW) and process the computations induced by active synaptic inputs falling under several TWs in parallel, leading to the proposed parallel time batching. It allows weight reuse across multiple time points and enhances the utilization of the systolic array with reduced idling of processing elements, overcoming the irregularity of sparse firing activities. We optimize the granularity of time-domain processing, i.e., the TW size, which significantly impacts the data reuse and utilization.

Lastly, we propose a novel technique and architecture that allow the exploitation of temporal information compression with structured sparsity and parallelism across time, and significantly improves data movement on a systolic array. We split a full range of temporal domain into several time windows (TWs) where a TW packs multiple time points, and encode the temporal information in each TW with Split-Time Temporal coding (STT) by limiting the number of spikes within a TW up to one. STT enables sparsification and structurization of irregular firing activities and dramatically reduces computational overhead while delivering competitive classification accuracy without a huge drop. To further improve the data reuse, we propose an Integration Through Time (ITT) technique that processes integration steps across different TWs in parallel with a systolic array.

In this dissertation, we provide unique and powerful solutions for the efficient acceleration of the spiking models with various datasets.