Low-power event-based analog front-ends (AFE) are a crucial component required to build efficient end-to-end neuromorphic processing systems for edge computing. Although several neuromorphic chips have been developed for implementing spiking neural networks (SNNs) and solving a wide range of sensory processing tasks, there are only a few general-purpose analog front-end devices that can be used to convert analog sensory signals into spikes and interfaced to neuromorphic processors. In this work, we present a novel, highly configurable analog front-end chip, denoted as SPAIC (signal-to-spike converter for analog AI computation), that offers a general-purpose dual-mode analog signal-to-spike encoding with delta modulation and pulse frequency modulation, with tunable frequency bands. The ASIC is designed in a 180 nm process. It supports and encodes a wide variety of signals spanning 4 orders of magnitude in frequency, and provides an event-based output that is compatible with existing neuromorphic processors. We validated the ASIC for its functions and present initial silicon measurement results characterizing the basic building blocks of the chip.

翻译：低功耗事件驱动模拟前端（AFE）是构建高效端到端神经形态处理系统以实现边缘计算的关键组件。尽管已有多种神经形态芯片被开发用于实现脉冲神经网络（SNN）并解决广泛的感知处理任务，但可用于将模拟感知信号转换为脉冲并与神经形态处理器接口的通用型模拟前端设备仍然很少。本文提出了一款新型高可配置模拟前端芯片，命名为SPAIC（用于模拟人工智能计算的信号到脉冲转换器），该芯片提供具有增量调制和脉冲频率调制（含可调频带）的通用双模式模拟信号到脉冲编码。该专用集成电路采用180纳米工艺设计，支持并编码频率跨越四个数量级的各类信号，并提供与现有神经形态处理器兼容的事件驱动输出。我们对该ASIC进行了功能验证，并展示了表征芯片基本构建模块的初始硅片测量结果。