With the remarkable progress that technology has made, the need for processing data near the sensors at the edge has increased dramatically. The electronic systems used in these applications must process data continuously, in real-time, and extract relevant information using the smallest possible energy budgets. A promising approach for implementing always-on processing of sensory signals that supports on-demand, sparse, and edge-computing is to take inspiration from biological nervous system. Following this approach, we present a brain-inspired platform for prototyping real-time event-based Spiking Neural Networks (SNNs). The system proposed supports the direct emulation of dynamic and realistic neural processing phenomena such as short-term plasticity, NMDA gating, AMPA diffusion, homeostasis, spike frequency adaptation, conductance-based dendritic compartments and spike transmission delays. The analog circuits that implement such primitives are paired with a low latency asynchronous digital circuits for routing and mapping events. This asynchronous infrastructure enables the definition of different network architectures, and provides direct event-based interfaces to convert and encode data from event-based and continuous-signal sensors. Here we describe the overall system architecture, we characterize the mixed signal analog-digital circuits that emulate neural dynamics, demonstrate their features with experimental measurements, and present a low- and high-level software ecosystem that can be used for configuring the system. The flexibility to emulate different biologically plausible neural networks, and the chip's ability to monitor both population and single neuron signals in real-time, allow to develop and validate complex models of neural processing for both basic research and edge-computing applications.

翻译：随着技术取得的显著进步，在边缘端靠近传感器处处理数据的需求急剧增加。用于此类应用的电子系统必须持续实时处理数据，并以尽可能小的能耗提取相关信息。实现支持按需、稀疏和边缘计算的传感信号持续处理的一种有前景的方法是借鉴生物神经系统的启发。遵循这一方法，我们提出了一种用于原型化实时事件驱动脉冲神经网络（SNN）的大脑启发平台。该系统支持对动态且逼真的神经处理现象进行直接仿真，例如短期可塑性、NMDA门控、AMPA扩散、稳态、脉冲频率适应、基于电导的树突区室和脉冲传输延迟。实现这些基元的模拟电路与用于路由和映射事件的低延迟异步数字电路配对。这种异步基础设施能够定义不同的网络架构，并提供直接的事件驱动接口，用于转换和编码来自事件驱动传感器和连续信号传感器的数据。本文描述了整体系统架构，表征了仿真神经动力学的混合信号模拟-数字电路，通过实验测量展示了其特性，并介绍了可用于配置系统的低层级与高层级软件生态系统。该系统在仿真不同生物合理性神经网络方面的灵活性，以及芯片实时监测群体和单个神经元信号的能力，使得为基础研究和边缘计算应用开发和验证复杂的神经处理模型成为可能。