论文标题
稀疏压缩尖峰神经网络加速器用于对象检测
Sparse Compressed Spiking Neural Network Accelerator for Object Detection
论文作者
论文摘要
受人脑启发的尖峰神经网络（SNN）最近因其相对简单且低功能的硬件而获得了流行，用于传输二进制尖峰和高度稀疏的激活图。但是，由于SNN包含额外的时间维信息，因此SNN加速器将需要更多的缓冲区，并且需要更长的时间才能推断，尤其是对于更困难的高分辨率对象检测任务。结果，本文提出了一个稀疏的压缩尖峰神经网络加速器，该加速器通过利用提出的门控一对一产品来利用激活图和权重的高稀疏性来实现低功率和高度并行模型的执行。神经网络的实验结果显示了IVS 3CLS数据集的混合时间（1,3）时间步骤的71.5 $ \％$映射。带有TSMC 28nm CMOS工艺的加速器可以达到1024 $ \ times $ 576@每秒29帧处理，在500MHz运行时，具有35.88台/W的能源效率和1.05mj的能源消耗。
Spiking neural networks (SNNs), which are inspired by the human brain, have recently gained popularity due to their relatively simple and low-power hardware for transmitting binary spikes and highly sparse activation maps. However, because SNNs contain extra time dimension information, the SNN accelerator will require more buffers and take longer to infer, especially for the more difficult high-resolution object detection task. As a result, this paper proposes a sparse compressed spiking neural network accelerator that takes advantage of the high sparsity of activation maps and weights by utilizing the proposed gated one-to-all product for low power and highly parallel model execution. The experimental result of the neural network shows 71.5$\%$ mAP with mixed (1,3) time steps on the IVS 3cls dataset. The accelerator with the TSMC 28nm CMOS process can achieve 1024$\times$576@29 frames per second processing when running at 500MHz with 35.88TOPS/W energy efficiency and 1.05mJ energy consumption per frame.
