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A 0.5V 55 uW 64X2-channel binaural silicon cochlea for event-driven stereo-audio sensing


Yang, Minhao; Chien, Chen-Han; Delbruck, Tobias; Liu, Shih-Chii (2016). A 0.5V 55 uW 64X2-channel binaural silicon cochlea for event-driven stereo-audio sensing. In: 2016 IEEE International Solid-State Circuits Conference (ISSCC), San Francisco, CA, USA, 31 January 2016 - 4 February 2016, 388-389.

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.

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.

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Additional indexing

Item Type:Conference or Workshop Item (Speech), refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:4 February 2016
Deposited On:26 Jan 2017 13:31
Last Modified:11 Sep 2017 00:56
Publisher:Proceedings of the 2016 IEEE International Solid-State Circuits Conference (ISSCC)
Series Name:2016 IEEE International Solid-State Circuits Conference (ISSCC)
Number of Pages:2
Publisher DOI:https://doi.org/10.1109/ISSCC.2016.7418070
Official URL:http://ieeexplore.ieee.org/document/7418070/

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