Spiker: an FPGA-optimized Hardware acceleration for Spiking Neural Networks
This work provides a hardware solution for time-constrained edge applications using SNNs, though it is incremental compared to existing optimized designs.
The paper tackled the problem of accelerating spiking neural networks (SNNs) for efficient inference by developing an FPGA-based hardware accelerator, achieving a performance of 215μs per image on the MINST dataset with an energy consumption of 13mJ per image.
Spiking Neural Networks (SNN) are an emerging type of biologically plausible and efficient Artificial Neural Network (ANN). This work presents the development of a hardware accelerator for a SNN for high-performance inference, targeting a Xilinx Artix-7 Field Programmable Gate Array (FPGA). The model used inside the neuron is the Leaky Integrate and Fire (LIF). The execution is clock-driven, meaning that the internal state of the neuron is updated at every clock cycle, even in absence of spikes. The inference capabilities of the accelerator are evaluated using the MINST dataset. The training is performed offline on a full precision model. The results show a good improvement in performance if compared with the state-of-the-art accelerators, requiring 215μs per image. The energy consumption is slightly higher than the most optimized design, with an average value of 13mJ per image. The test design consists of a single layer of four-hundred neurons and uses around 40% of the available resources on the FPGA. This makes it suitable for a time-constrained application at the edge, leaving space for other acceleration tasks on the FPGA.