论文标题
连续时间模拟滤波器用于音频边缘智能：回顾电路设计
Continuous-Time Analog Filters for Audio Edge Intelligence: Review on Circuit Designs
论文作者
论文摘要
Edge Audio设备可以通过在传输到云之前对设备上的输入语音进行预加工输入语音来减少数据带宽要求。由于需要边缘设备来确保始终在运行中，因此其严格的功率约束构成了一些设计挑战，并迫使IC设计师寻找使用低备用功率的解决方案。一种有希望的生物启发的方法是将连续的模拟滤波器通道与较小的记忆足迹深度神经网络相结合，该网络在边缘任务（例如关键字斑点）上进行了训练，从而允许将所有块嵌入IC中。本文回顾了连续的模拟滤清器电路的历史背景，这些电路已被用作当前边缘音频设备的功能提取器。从将基本双Quad滤波器解释为双关联器 - 环拓扑结构开始，我们介绍了二阶低通和带通滤波器设计的进展，从基于OTA到基于Source-proser的体系结构。我们还得出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用法。
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.
