Minhao Yang

Cette personne n’est plus à l’EPFL

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Publications associées (5)

Comparison of Two in Pixel Source Follower Schemes for Deep Subelectron Noise CMOS Image Sensors

Christian Enz, Minhao Yang, Assim Boukhayma, Antonino Caizzone, Daniel Mathias Bold, Andrea Kraxner

This paper compares two in-pixel source follower stage designs for low noise CMOS image sensors embedded both on a same 5 mm by 5 mm chip fabricated in a 180 nm CIS process. The presented chip embeds two pixel variants, one based on a body-effect-canceled ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Nanowatt Acoustic Inference Sensing Exploiting Nonlinear Analog Feature Extraction

Christian Enz, Minhao Yang, Jun Zhang

Ultralow-power sensing with inference functionality embedded in sensor nodes is essential for enabling the emerging pervasive intelligence. For acoustic inference sensing, the feature extraction can take advantage of power-efficient analog circuits. Howeve ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

A 510nW 0.41V Low-Memory Low-Computation Keyword-Spotting Chip Using Serial FFT-Based MFCC and Binarized Depthwise Separable Convolutional Neural Network in 28nm CMOS

Tao Wang, Minhao Yang, Jiangyan Yang, Yicheng Lu, Jiaming Xu

IEEE2020

Design of an Always-On Deep Neural Network-Based 1-mu W Voice Activity Detector Aided With a Customized Software Model for Analog Feature Extraction

Minhao Yang

This paper presents an ultra-low-power voice activity detector (VAD). It uses analog signal processing for acoustic feature extraction (AFE) directly on the microphone output, approximate event-driven analog-to-digital conversion (ED-ADC), and digital deep ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019

Ultra-Low-Power Intelligent Acoustic Sensing using Cochlea-Inspired Feature Extraction and DNN Classification

Christian Enz, Minhao Yang

We present our recent progress in ultra-low-power intelligent acoustic sensing that harnesses the high power and energy efficiency of cochlea-like analog feature extraction and binarized neural network classification. Compared with conventional methods inc ...

IEEE2019

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