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：随着技术的显著进步，在边缘设备上靠近传感器处理数据的需求急剧增加。这些应用中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、稀疏和边缘计算的持续感知信号处理的一种有前景的方法是借鉴生物神经系统的启发。遵循这一思路，我们提出了一种灵感来源于大脑的平台，用于原型实现实时基于事件的脉冲神经网络（SNNs）。所提出的系统支持动态和逼真的神经处理现象的直接仿真，例如短期可塑性、NMDA门控、AMPA扩散、稳态、脉冲频率适应性、基于电导的树突分区以及脉冲传输延迟。实现这些基元的模拟电路与低延迟异步数字电路配对，用于事件的路由和映射。这种异步基础设施使得能够定义不同的网络架构，并提供直接的基于事件的接口，用于转换和编码来自基于事件的传感器和连续信号传感器的数据。本文描述了整个系统架构，表征了模拟神经动力学的混合信号模数电路，通过实验测量展示了它们的特性，并介绍了可用于配置系统的低层次和高层次软件生态系统。仿真不同生物学可信神经网络的能力，以及芯片实时监测群体和单个神经元信号的功能，使得能够为基础研究和边缘计算应用开发并验证复杂的神经处理模型。