课题基金 / 基金详情

Phase I: laconic: a deep learning accelerator for energy efficiency and high-performance for edge devices

Phase I: laconic: a deep learning accelerator for energy efficiency and high-performance for edge devices
第一阶段:laconic:边缘设备能效和高性能的深度学习加速器
批准号:
530383-2018
负责人:
Moshovos, Andreas
金额:
$8.2万
依托单位:
依托单位国家:
加拿大
项目类别:
Idea to Innovation
财政年份:
2018
资助国家:
加拿大
项目状态:
已结题
起止时间:
2018-01-01 至 2019-12-31

项目摘要

项目成果

Moshovos, Andreas的其他基金

相关文献

中文摘要
翻译
点击翻译按钮获取中文摘要
英文摘要
Machine Learning is transforming commerce, medicine, science, and virtually every aspect of modern life. Canada has been at the forefront of innovation in Machine Learning as we have been steadily investing in the area for decades. Machine learning would not have been possible without the computing power that modern commodity hardware was able to deliver. Further innovation will have to rely heavily in advances in computational power and thus efficiency.** Over the course of 4+ years we have been developing computing hardware concepts and device designs that deliver an order of magnitude faster data processing capability and energy efficiency than commodity hardware for Deep Learning (DL). With our 20+ years of experience in designing techniques for high-performance computing systems, we leapfrogged over first generation optimizations and instead invested in high-risk complementary techniques. Our approach was to identify and exploit typical yet fundamental properties of the value stream of Deep Learning. The results surpassed our expectations.** Our efforts culminated into Laconic, a design that outperforms other accelerators by an order of magnitude. Laconic exploits fundamental low-level value properties in Deep Learning models and requires no help from the programmer. While Laconic can be configured to target multiple market segments, we will target "edge" (embedded/mobile) applications as our previously-funded NSERC Market Assessment has shown great potential there. Our key competitive advantage is that Laconic will allow: 1) the deployment of more sophisticated and accurate DL networks, 2) a drastic reduction in energy consumption and thus improvement in up-time for mobile devices while making the system more portable and usable in a variety of scenarios. We have proven the potential of Laconic through industry standard methodologies for early design exploration. We are seeking Phase I support to build a proof-of-concept demonstration prototype which is essential for attracting interest from partner and investment organizations (some already express strong interest).****
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Deep Learning Hardware: Enabling the next wave of applications and innovation
  • 批准号:
    RGPIN-2017-06064
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $6.85万
  • 财政年份:
    2021
  • 负责人:
    Moshovos, Andreas
  • 依托单位:
NSERC COHESA: Computing Hardware for Emerging Intelligent Sensory Applications
  • 批准号:
    485577-2015
  • 项目类别:
    Strategic Network Grants Program
  • 资助金额:
    $81.96万
  • 财政年份:
    2021
  • 负责人:
    Moshovos, Andreas
  • 依托单位:
A Business / Market Opportunity Assessment for Hardware Concepts & Device Designs for Brain-Machine Interfacing
  • 批准号:
    571002-2022
  • 项目类别:
    Idea to Innovation
  • 资助金额:
    $1.08万
  • 财政年份:
    2021
  • 负责人:
    Moshovos, Andreas
  • 依托单位:
NSERC COHESA: Computing Hardware for Emerging Intelligent Sensory Applications
  • 批准号:
    485577-2015
  • 项目类别:
    Strategic Network Grants Program
  • 资助金额:
    $81.96万
  • 财政年份:
    2020
  • 负责人:
    Moshovos, Andreas
  • 依托单位: