Toward Energy-Efficient, Bio-Inspired Circuits and Systems for Error-Resilient and Biomedical Applications

面向防错和生物医学应用的节能、仿生电路和系统

基本信息

  • 批准号:
    RGPIN-2015-06007
  • 负责人:
  • 金额:
    $ 1.82万
  • 依托单位:
  • 依托单位国家:
    加拿大
  • 项目类别:
    Discovery Grants Program - Individual
  • 财政年份:
    2017
  • 资助国家:
    加拿大
  • 起止时间:
    2017-01-01 至 2018-12-31
  • 项目状态:
    已结题

项目摘要

Many of us get our computers, laptops or phones replaced every few years. Technology advances due to the continuous miniaturization of electronic devices, such that a larger number of devices can be packed into a single chip, while the cost has been kept relatively stable. This trend has slowed down, however, and is predicted to end in less than a decade. A transistor, the basic functional unit in a circuit, is now sized in just a few nanometers, that is, a few billionth of a meter. At such a small scale, it is difficult to fabricate all transistors uniformly and make them operate reliably. The current method to ensure a reliable operation is to apply a larger power than it is often necessary, so electronics still consume a lot of energy. On the other hand, many computer applications, such as multimedia, voice recognition and web search, do not always require a fully accurate result and a “good-enough” result is often sufficient due to many factors such as human perceptual limitations. This class of applications is considered imprecision-tolerant or error-resilient. One objective of this research program is to address the energy-efficiency and error-resilience issues in nanometer-scale electronics by developing new and innovative computational structures that employ approximate, stochastic and the brain-inspired neuromorphic computing techniques. These new techniques allow computing systems to trade off quality for energy.
我们中的许多人每隔几年就会更换一次电脑、笔记本电脑或手机。由于电子设备的不断小型化,技术进步,使得更大数量的设备可以封装到单个芯片中,而成本保持相对稳定。然而,这一趋势已经放缓,预计将在不到十年的时间内结束。晶体管,电路中的基本功能单元,现在的尺寸只有几纳米,也就是几十亿分之一米。在如此小的规模下,很难均匀地制造所有晶体管并使它们可靠地工作。目前确保可靠运行的方法是施加比通常所需的更大的功率,因此电子设备仍然消耗大量能量。另一方面,许多计算机应用,例如多媒体、语音识别和网络搜索,并不总是需要完全准确的结果,并且由于诸如人类感知限制的许多因素,“足够好”的结果通常是足够的。这类应用程序被认为是不精确的容忍或错误弹性。该研究计划的一个目标是通过开发新的和创新的计算结构,采用近似,随机和大脑启发的神经形态计算技术,解决纳米级电子产品的能效和容错问题。这些新技术允许计算系统在质量和能量之间进行权衡。

项目成果

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Han, Jie其他文献

Design and Preparation of Polyimide/TiO(2)@MoS(2) Nanofibers by Hydrothermal Synthesis and Their Photocatalytic Performance.
  • DOI:
    10.3390/polym14163230
  • 发表时间:
    2022-08-09
  • 期刊:
  • 影响因子:
    5
  • 作者:
    Chang, Zhenjun;Sun, Xiaoling;Liao, Zhengzheng;Liu, Qiang;Han, Jie
  • 通讯作者:
    Han, Jie
Interaction between Her2 and Beclin-1 Proteins Underlies a New Mechanism of Reciprocal Regulation
  • DOI:
    10.1074/jbc.m113.461350
  • 发表时间:
    2013-07-12
  • 期刊:
  • 影响因子:
    4.8
  • 作者:
    Han, Jie;Hou, Wen;Rabinowich, Hannah
  • 通讯作者:
    Rabinowich, Hannah
It is time to acknowledge coronavirus transmission via frozen and chilled foods: Undeniable evidence from China and lessons for the world.
  • DOI:
    10.1016/j.scitotenv.2023.161388
  • 发表时间:
    2023-04-10
  • 期刊:
  • 影响因子:
    9.8
  • 作者:
    Dai, Han;Tang, Hao;Sun, Wen;Deng, Shihai;Han, Jie
  • 通讯作者:
    Han, Jie
Gold Nanorods/Polypyrrole/m-SiO2 Core/Shell Hybrids as Drug Nanocarriers for Efficient Chemo-Photothermal Therapy
金纳米棒/聚吡咯/m-SiO2核/壳杂化物作为药物纳米载体用于高效化学光热治疗
  • DOI:
    10.1021/acs.langmuir.8b02667
  • 发表时间:
    2018-12-04
  • 期刊:
  • 影响因子:
    3.9
  • 作者:
    Wang, Juan;Han, Jie;Guo, Rong
  • 通讯作者:
    Guo, Rong
miR-29a inhibits proliferation, invasion, and migration of papillary thyroid cancer by targeting DPP4
  • DOI:
    10.2147/ott.s201532
  • 发表时间:
    2019-01-01
  • 期刊:
  • 影响因子:
    4
  • 作者:
    Wang, Yufei;Han, Jie;Zhang, Guochao
  • 通讯作者:
    Zhang, Guochao

Han, Jie的其他文献

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{{ truncateString('Han, Jie', 18)}}的其他基金

Approximate and Stochastic Computing Systems
近似和随机计算系统
  • 批准号:
    RGPIN-2020-06572
  • 财政年份:
    2022
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Efficient computing systems for deep learning and combinatorial optimization
用于深度学习和组合优化的高效计算系统
  • 批准号:
    552712-2020
  • 财政年份:
    2021
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Alliance Grants
Approximate and Stochastic Computing Systems
近似和随机计算系统
  • 批准号:
    RGPIN-2020-06572
  • 财政年份:
    2021
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Low-power and high-performance circuit modules for digital signal processing, wireless communications and deep learning
用于数字信号处理、无线通信和深度学习的低功耗高性能电路模块
  • 批准号:
    561173-2020
  • 财政年份:
    2021
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Alliance Grants
Approximate and Stochastic Computing Systems
近似和随机计算系统
  • 批准号:
    RGPIN-2020-06572
  • 财政年份:
    2020
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Efficient computing systems for deep learning and combinatorial optimization
用于深度学习和组合优化的高效计算系统
  • 批准号:
    552712-2020
  • 财政年份:
    2020
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Alliance Grants
Low-power and high-performance circuit modules for digital signal processing, wireless communications and deep learning
用于数字信号处理、无线通信和深度学习的低功耗高性能电路模块
  • 批准号:
    561173-2020
  • 财政年份:
    2020
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Alliance Grants
Toward Energy-Efficient, Bio-Inspired Circuits and Systems for Error-Resilient and Biomedical Applications
面向防错和生物医学应用的节能、仿生电路和系统
  • 批准号:
    RGPIN-2015-06007
  • 财政年份:
    2019
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
An Integrated Testing System for SKAA
SKAA 综合测试系统
  • 批准号:
    543453-2019
  • 财政年份:
    2019
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Engage Grants Program
Toward Energy-Efficient, Bio-Inspired Circuits and Systems for Error-Resilient and Biomedical Applications
面向防错和生物医学应用的节能、仿生电路和系统
  • 批准号:
    RGPIN-2015-06007
  • 财政年份:
    2018
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual

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