Spiking neural networks (SNNs) have attracted widespread interest due to their event-driven and low-power nature. Compared to Artificial Neural Networks (ANNs), SNNs have time dimension information and present more realistic brain-inspired computing models. However, it is challenging to deploy sparse spiking neuron network models on dense neuromorphic processors. In this paper, a spiking neural network accelerator with sparse spike and weight is presented, using ping-pong architecture to improve system data throughput. To reduce the inference delay, the proposed accelerator supports the decoupling of calculation of membrane potential and leaky integrate-and-fire (LIF) dynamics computing in the feedforward neural networks. Implemented on Xilinx Kintex UltraScale FPGA, the accelerator can achieve the peak performance of 65.7 GSOP/s and the energy efficiency of 41.7 GSOP/W in the task of classifying MNIST dataset. Under the full load, the whole system can run ping-pong when more than 43 time steps are calculated at a time.

Spiking neural networks (SNNs) have widely drew attention of recent research. With brain-spired dynamics and spike-based communication, SNN is supposed to be a more energy-efficient neural network than existing artificial neural network (ANN). To make better use of the temporal sparsity of spikes and spatial sparsity of weights in SNN, this paper presents a sparse SNN accelerator. It adopts a novel self-adaptive spike compressing and decompressing (SASCD) mechanism for different input spike sparsity, as well as on-chip compressed weight storage and processing. We implement the octa-core design on field programmable gate array (FPGA). The results demonstrate a peak performance of 35.84 GSOPs/s, which is equivalent to 358.4 GSOPs/s in dense SNN accelerators for 90% weight sparsity. For the single-layer perceptron model in rate coding implemented on the hardware, SASCD reduces the time step intervals from 2.15 $\mu$ s to 0.55 $\mu$ s.

Spiking neural networks (SNNs) have been witnessing the developing trends to reduce the model size and improve the hardware efficiency for area- and energy-based applications, which are processed by model pruning and data compressions. However, it is challenging to exploit the unstructured sparsity of SNNs for the dense neuromorphic processors. In this article, we present an efficient sparse SNN accelerator (ESSA), which leverages both the temporal sparsity of spike events and the spatial sparsity of weights in SNN inference. It provides both the compressed weights for sparse SNNs and the uncompressed weights for compact SNNs. The self-adaptive spike compression is proposed for sparse spike scenarios, leading to the improvement of throughput by$3.2\times $. ESSA executes a flexible fan-in–fan-out tradeoff by using combinable dendrites, which overcomes the fan-in limitation in neuromorphic systems. Furthermore, a low-latency intrachip spike multicast method is adopted to reduce the resource overhead. Implemented on the Xilinx Kintex Ultrascale field-programmable gate array (FPGA), ESSA achieves an equivalent performance of 253.1 GSOP/s and an energy efficiency of 32.1 GSOP/W for 75% weight sparsity at 140 MHz. The implementation of a four-layer fully connected SNN is expected to perform$2.6~\mu \text{s}$per time step and the energy consumption is$14.6~\mu \text{J}$. Our results demonstrate that ESSA outperforms several state-of-the-art application-specific integrated circuit (ASIC) or FPGA neuromorphic processors.

Neuromorphic hardware platforms inspired by human brain have emerged as novel non von Neumann computing architectures. They were proved excellent platforms for spiking neural network (SNN) implementations. However, implementing artificial neural networks (ANNs) on existing neuromorphic hardware platforms is still a daunting task because of critical limitations on coding scheme, maximum of fan-in, and highest weight precision in them. In this paper, we introduce a neuromorphic processor developed for various neural networks implementations including ANNs and SNNs. We employ spatio-temporal coding scheme based on spike events. By combining low-precision dendrites, the chip can implement weight precision between 1 bit and 8 bits and scalable fan-in. The 3.66-mm2chip fabricated in 28-nm CMOS with a maximum fan-in of 72 K per neuron demonstrates unprecedented compatibility with ANN applications compared to previously-proposed neuromorphic chips.

Event-based spiking neural network (SNN) has displayed a promising prospect to realize real-time, efficient and intelligent hardware platforms. Whereas great efforts are still being appealed to explore the possibility of introducing online learning abilities to neuromorphic systems. In this paper, a 28-nm CMOS neuromorphic processor is presented, fulfilling online learning by adopting counter and lookup table (LUT) based spike-timing-dependent plasticity (STDP) rule. Designed to work at high-precision scenarios, the presented processor integrates up to 1024 neurons and 256K signed 9-bit synapses. It also ensures chip array interconnection to fit large neural networks. Moreover, by utilizing the sparse property of spike events to minimize activity rate, the typical power consumption is further reduced to 3.348mW for training MNIST dataset.