A 0.5V 55μW 64×2-channel binaural silicon cochlea for event-driven stereo-audio sensing
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Event-driven DSPs have the advantage of activity-dependent power consumption , and event-driven neural networks have shown superior power efficiency in real-time recognition tasks . A bio-inspired silicon cochlea  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  and . High-quality event encoding can be implemented as level-crossing (LC) ADCs, but the circuits are area- and power-inefficient . Asynchronous delta modulation, the original form of LC sampling, on the other hand can be compactly realized even in small pixels of vision sensors . Traditional audio processing employs digital FFTs and BPFs after signal acquisition by high-precision ADCs. However, it has been shown in  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 55μW 64×2-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. Show more
Book title2016 IEEE International Solid-State Circuits Conference (ISSCC)
Journal / seriesDigest of Technical Papers / IEEE International Solid State Circuits Conference
Pages / Article No.
Organisational unit03453 - Douglas, Rodney J.
03774 - Hahnloser, Richard H.R. / Hahnloser, Richard H.R.
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