Case Study of Tactile Sensors: System-level Approach to Analog In-sensor Computing

Min Young Mun, Sei Joon Kim, Seok Ju Yun, Sang Joon Kim

2023 International Electron Devices Meeting (IEDM)（2023）

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Abstract

We propose a novel analog in-sensor computing architecture, enhancing the existing fully parallel analog processing system by incorporating analog circuits to store layer outputs. Through simulations using tactile sensors, we demonstrate a 65% reduction in area and superior power efficiency of 660 TOPS/W, twice as efficient as the previous work. Our findings emphasize the cost-effectiveness and power efficiency of analog in-sensor computing for Convolutional Neural Networks(CNNs) model execution.

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Key words

Tactile Sensor,In-sensor Computing,Convolutional Neural Network,Artificial Neural Network,Output Layer,Reduction In Area,Convolutional Neural Network Model,Power Efficiency,Analog Circuits,Convolutional Layers,System Architecture,Precise Model,Convolutional Neural Network Architecture,Convolutional Block,Multi-task Learning,Subsequent Layers,Sensor Module,Number Of Arrays,Crossbar Array,Tactile Task,Shared Layers

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