Approximate and Stochastic Computing Systems

近似和随机计算系统

基本信息

  • 批准号:
    RGPIN-2020-06572
  • 负责人:
  • 金额:
    $ 2.4万
  • 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:
    2022
  • 资助国家:
    加拿大
  • 起止时间:
    2022-01-01 至 2023-12-31
  • 项目状态:
    已结题

项目摘要

Current computer systems still consume a significant amount of power, regardless of the size of the system: given it a smart phone, a personal computer or a computer server. The reason is two-fold: 1) the tiny sizes of basic electronic devices (at a dimension of the nanometer scale) make them susceptible to variation and temporary errors due to manufacturing and environmental factors, so larger-than-necessary voltages and currents are required to ensure the operational reliability; and 2) computing applications have increasingly become complex, such as multimedia, that involve a significant number of arithmetic operations. As a result, energy efficiency has become a paramount concern for current computer systems. The conflict between reliability and energy efficiency seems to be inevitable and presents significant design challenges. On the other hand, however, many computing tasks show a common characteristic of being imprecision-tolerant or error-resilient, particularly during the intermediate computing process. It is an especially important feature in many emerging applications such as image/video processing, pattern recognition and machine learning. The objective of this research program is to develop a new class of computing systems that employs approximate computing (AC) and stochastic computing (SC) techniques for energy-efficient and high-performance processing. AC leverages the error resilience in many applications and employs deliberate and deterministic designs to deliver imprecise but good-enough results, whereas SC uses simplistic hardware with random binary bit streams for producing meaningful statistics in the computed result. Considerable effort has been devoted to the development of basic circuit elements for AC and to the design and implementation of basic building blocks for SC. A challenge, however, is to integrate various circuit components into an AC and/or SC system for low-power and high-performance operation. To address this challenge, the effective integration of various AC and SC circuit components into a larger system will be the primary focus of this research. The design will be aimed at conventional digital signal processing in embedded and mobile systems, as well as emerging brain-inspired computing systems that explore the organization and functions of neurons and connecting synapses at different levels of hierarchy and abstraction. Two essential classes of applications will be considered: 1) multimedia, including image, audio and video processing; and 2) image and voice recognition using effective machine-learning models, including neural networks. Addressing a fundamentally challenging issue, i.e., energy efficiency in computer systems, this research program will produce useful results for the electronics and information industry. It will also provide valuable training opportunities for highly qualified personnel with skills demanded by and crucial to the long-term growth of the Canadian economy.
当前的计算机系统仍然消耗大量的电力,无论系统的大小:智能手机,个人计算机或计算机服务器。原因有二:1)基本电子器件的微小尺寸(在纳米尺度的尺寸)使得它们容易受到由于制造和环境因素引起的变化和暂时误差的影响,因此需要比必要的更大的电压和电流来确保操作可靠性;以及2)计算应用已经变得越来越复杂,例如涉及大量算术运算的多媒体。因此,能量效率已经成为当前计算机系统的首要关注点。 可靠性和能源效率之间的冲突似乎是不可避免的,并提出了重大的设计挑战。然而,另一方面,许多计算任务显示出一个共同的特性,即不精确容忍或错误弹性,特别是在中间计算过程中。它在许多新兴应用中是一个特别重要的功能,如图像/视频处理,模式识别和机器学习。 该研究计划的目标是开发一类新的计算系统,采用近似计算(AC)和随机计算(SC)技术进行节能和高性能处理。AC在许多应用中利用错误恢复能力,并采用故意和确定性的设计来提供不精确但足够好的结果,而SC使用具有随机二进制比特流的简单硬件来在计算结果中产生有意义的统计数据。人们投入了大量精力来开发交流基本电路元件以及设计和实现SC基本构建模块。然而,一个挑战是将各种电路元件集成到交流和/或SC系统中,以实现低功耗和高性能操作。 为了应对这一挑战,将各种AC和SC电路组件有效集成到一个更大的系统中将是本研究的主要重点。该设计将针对嵌入式和移动的系统中的传统数字信号处理,以及新兴的大脑启发计算系统,这些系统探索神经元的组织和功能,并在不同层次和抽象层次上连接突触。将考虑两类基本应用:1)多媒体,包括图像,音频和视频处理; 2)使用有效的机器学习模型,包括神经网络的图像和语音识别。 解决一个具有根本挑战性的问题,即,电脑系统的能源效益,这项研究计划将为电子及资讯业带来有用的成果。它还将为具有加拿大经济长期增长所需技能和关键技能的高素质人员提供宝贵的培训机会。

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

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:
    $ 2.4万
  • 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:
    $ 2.4万
  • 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:
    $ 2.4万
  • 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:
    $ 2.4万
  • 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:
    $ 2.4万
  • 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:
    $ 2.4万
  • 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:
    $ 2.4万
  • 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:
    $ 2.4万
  • 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:
    $ 2.4万
  • 项目类别:
    Discovery Grants Program - Individual
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:
    2017
  • 资助金额:
    $ 2.4万
  • 项目类别:
    Discovery Grants Program - Individual

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Development of a Linear Stochastic Model for Wind Field Reconstruction from Limited Measurement Data
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Approximate and Stochastic Computing Systems
近似和随机计算系统
  • 批准号:
    RGPIN-2020-06572
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    2020
  • 资助金额:
    $ 2.4万
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    Discovery Grants Program - Individual
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