Design of Low-Power SoC for Wearable Healthcare Device

Author:

Kim Ji Kwang1,Oh Jung Hwan1,Hwang Gwan Beom1,Gwon Oh Seong1,Lee Seung Eun1ORCID

Affiliation:

1. Department of Electronic Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea

Abstract

In wearable devices, power consumption is a serious issue since wearable devices must maintain the power-on state at any time. In healthcare system, a variety of signal processing operations occupy a large portion of overall workload because it has periodic and heavy computational workloads. In this paper, we propose a low-power System on Chip (SoC) architecture for wearable healthcare devices. In order to reduce power consumption of processor, we design a hardware accelerator that handles signal processing and provides computation offloading. Furthermore, to minimize the area and maximize the performance of the accelerator, we optimize the operation bit-width by analyzing the frequency response. The low-power healthcare SoC was fabricated with 0.11[Formula: see text][Formula: see text]m CMOS process. Finally, we measured the power consumption of our chip and verified the applicability of the digital filter accelerator, which reduces the energy consumption for embedded processor.

Funder

Ministry of Education

Publisher

World Scientific Pub Co Pte Lt

Subject

Electrical and Electronic Engineering,Hardware and Architecture,Electrical and Electronic Engineering,Hardware and Architecture

Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. ASIC Chip Design For Healthcare System;2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI);2024-03-14

2. Investigation and Analysis of Power Performance Area (PPA) Cards of Digital Multiplier Architectures;Journal of Circuits, Systems and Computers;2022-08-25

3. Development and Analysis of Novel Mesh of Tree-based embedded FPGA;The Journal of Supercomputing;2022-05-22

4. Machine Learning for Healthcare Wearable Devices: The Big Picture;Journal of Healthcare Engineering;2022-04-18

5. Data-Driven Self-Learning Controller Design Approach for Power-Aware IoT Devices based on Double Q-Learning Strategy;2021 IEEE Symposium Series on Computational Intelligence (SSCI);2021-12-05

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3