Spiking Cochlea with System-level Local Automatic Gain Control

• URL: http://arxiv.org/abs/2202.06707v1
• Date: Mon, 14 Feb 2022 13:58:13 GMT
• Title: Spiking Cochlea with System-level Local Automatic Gain Control
• Authors: Ilya Kiselev, Chang Gao, Shih-Chii Liu
• Abstract summary: We present an alternative system-level algorithm that implements channel-specific automatic gain control (AGC) in a silicon spiking cochlea. Because this AGC mechanism only needs counting and adding operations, it can be implemented at low hardware cost in a future design. We evaluate the impact of the local AGC algorithm on a classification task where the input signal varies over 32 dB input range.
• Score: 13.532394494130468
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Including local automatic gain control (AGC) circuitry into a silicon cochlea design has been challenging because of transistor mismatch and model complexity. To address this, we present an alternative system-level algorithm that implements channel-specific AGC in a silicon spiking cochlea by measuring the output spike activity of individual channels. The bandpass filter gain of a channel is adapted dynamically to the input amplitude so that the average output spike rate stays within a defined range. Because this AGC mechanism only needs counting and adding operations, it can be implemented at low hardware cost in a future design. We evaluate the impact of the local AGC algorithm on a classification task where the input signal varies over 32 dB input range. Two classifier types receiving cochlea spike features were tested on a speech versus noise classification task. The logistic regression classifier achieves an average of 6% improvement and 40.8% relative improvement in accuracy when the AGC is enabled. The deep neural network classifier shows a similar improvement for the AGC case and achieves a higher mean accuracy of 96% compared to the best accuracy of 91% from the logistic regression classifier.

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