Abstract
Event-driven DSPs have the advantage of activity-dependent power consumption [1], and event-driven neural networks have shown superior power efficiency in real-time recognition tasks [2]. A bio-inspired silicon cochlea [3] functionally transforms sound input into multi-frequency-channel asynchronous event output, and hence is the natural candidate for the audio sensing frontend of event-driven signal processing systems like [1] and [2]. High-quality event encoding can be implemented as level-crossing (LC) ADCs, but the circuits are area- and power-inefficient [1]. Asynchronous delta modulation, the original form of LC sampling, on the other hand can be compactly realized even in small pixels of vision sensors [4]. Traditional audio processing employs digital FFTs and BPFs after signal acquisition by high-precision ADCs. However, it has been shown in [5] that for classification tasks like voice activity detection (VAD), good accuracy can still be attained when filtering is performed using low-power analog BPFs. This paper presents a 0.5V 55uW 64x2-channel binaural silicon cochlea aiming for ultra-low-power IoE applications like event-driven VAD, sound source localization, speaker identification and primitive speech recognition. The source-follower-based BPF and the asynchronous delta modulator (ADM) with adaptive self-oscillating comparison for event encoding are highlighted for the advancement of the system power efficiency.
Yang, Minhao