Edge audio devices can reduce data bandwidth requirements by pre-processing input speech on the device before transmission to the cloud. As edge devices are required to ensure always-on operation, their stringent power constraints pose several design challenges and force IC designers to look for solutions that use low standby power. One promising bio-inspired approach is to combine the continuous-time analog filter channels with a small memory footprint deep neural network that is trained on edge tasks such as keyword spotting, thereby allowing all blocks to be embedded in an IC. This paper reviews the historical background of the continuous-time analog filter circuits that have been used as feature extractors for current edge audio devices. Starting from the interpretation of a basic biquad filter as a two-integrator-loop topology, we introduce the progression in the design of second-order low-pass and band-pass filters ranging from OTA-based to source-follower-based architectures. We also derive and analyze the small-signal transfer function and discuss their usage in edge audio applications.

翻译：边缘音频设备可在将输入语音传输到云端之前，在设备端进行预处理，从而降低数据带宽需求。由于边缘设备需支持持续工作模式，其严格的功耗约束带来了诸多设计挑战，促使集成电路设计师寻找低待机功耗的解决方案。一种具有潜力的仿生方法是将连续时间模拟滤波器通道与占用内存较小的深度神经网络相结合，并针对关键词检测等边缘任务进行训练，从而将所有模块集成到单个IC中。本文回顾了当前边缘音频设备中用作特征提取器的连续时间模拟滤波器电路的历史背景。从基本双二次滤波器作为双积分器环路拓扑结构的解释出发，我们介绍了从OTA型到源极跟随器型架构的二阶低通和带通滤波器设计的演变过程。我们还推导并分析了小信号传输函数，并讨论了它们在边缘音频应用中的使用。