This paper presents a spiking neural network (SNN) accelerator made using fully open-source EDA tools, process design kit (PDK), and memory macros synthesized using OpenRAM. The chip is taped out in the 130 nm SkyWater process and integrates over 1 million synaptic weights, and offers a reprogrammable architecture. It operates at a clock speed of 40 MHz, a supply of 1.8 V, uses a PicoRV32 core for control, and occupies an area of 33.3 mm^2. The throughput of the accelerator is 48,262 images per second with a wallclock time of 20.72 us, at 56.8 GOPS/W. The spiking neurons use hysteresis to provide an adaptive threshold (i.e., a Schmitt trigger) which can reduce state instability. This results in high performing SNNs across a range of benchmarks that remain competitive with state-of-the-art, full precision SNNs. The design is open sourced and available online: https://github.com/sfmth/OpenSpike

翻译：本文介绍了一种脉冲神经网络（SNN）加速器，该加速器完全采用开源EDA工具、工艺设计套件（PDK）及通过OpenRAM综合生成的内存宏单元实现。芯片基于130纳米SkyWater工艺流片，集成了超过100万突触权重，并具备可重构架构。其工作时钟频率为40 MHz，电源电压1.8 V，采用PicoRV32内核进行控制，芯片面积为33.3 mm²。加速器吞吐量达每秒48,262张图像，墙钟时间为20.72微秒，能效为56.8 GOPS/W。脉冲神经元采用迟滞机制实现自适应阈值（即施密特触发器），可降低状态不稳定性。这使得该SNN在一系列基准测试中保持高性能，与当前最先进的全精度SNN相比具有竞争力。本设计已开源，可通过以下链接获取：https://github.com/sfmth/OpenSpike