EDITH: Efficient Design for Intelligent devices exploiting emerging TecHnologies

EDITH:利用新兴技术的智能设备的高效设计

基本信息

  • 批准号:
    RGPIN-2019-06965
  • 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

The current trend in Internet of Things is to design processing subsystems on the edge that are smarter, smaller and more autonomous. Therefore, it is expected that the Internet of Things of the future will not be composed of passive devices sending data to a server, it will be rather like a distributed computing fabric, where many of the IoT devices and subsystems themselves will process and analyze data in order to make autonomous decisions. Typical target applications include video-processing systems integrated in smart cars, video surveillance cameras, mobile phones, home personal assistants, drones, healthcare devices, industrial monitoring and control, etc. The key enablers for this new paradigm will be the innovative programmable devices and subsystems able to execute multiple complex algorithms including deep learning algorithms (ex. Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN)), under tight timing and power constraints. This new class of algorithms involve repeated multiplications of matrices, which are computationally intensive and require high data bandwidths. In addition, the power efficiency requirements are very high: up to 10 Tera Operations Per Second (TOPS) per Watt. Conventional computing architectures are not well suited to these new requirements. In this context, we aim to innovate at the architecture level of devices, by considering emerging technologies like silicon-photonics that have progressively attracted more and more attention for their use in tackling the high-power consumption and low bandwidth issues in conventional technologies. These technologies can be integrated into proposed application-specific architectures tuned to implement deep learning algorithms like CNN and RNN by exploiting the low power consumption and the unequalled speed and bandwidth specific to silicon-photonics. We will define an architecture exploration flow that supports the integration of photonics-based components, distributed parallel memories, and application-specific programmable processors that coordinate the data movement and computation. We will develop new performance models that integrate the components mentioned above. We will also propose efficient programming and deployment models for deep learning algorithms. In this models, security will be one of the cetric metrics.  The novelty of the proposed contributions resides mainly in: (1) the architecture-level and system-level view of the intelligent devices based on the silicon-photonics emerging technologies, (2) the combining of top-down and bottom up approach and (3) the perfect complementarity with the advanced physical-level research proposing innovative emergent technologies.
物联网目前的趋势是在边缘设计更智能、更小、更自主的处理子系统。因此,预计未来的物联网将不会由向服务器发送数据的被动设备组成,它更像是一个分布式计算结构,其中许多物联网设备和子系统本身将处理和分析数据,以便做出自主决策。典型的目标应用包括集成在智能汽车、视频监控摄像头、移动电话、家庭个人助理、无人机、医疗设备、工业监控等中的视频处理系统。这种新范式的关键推动者将是创新的可编程设备和子系统,它们能够在严格的时间和功率限制下执行多种复杂算法,包括深度学习算法(例如卷积神经网络(CNN)、循环神经网络(RNN))。这类新的算法涉及矩阵的重复乘法,这是计算密集型的,需要高数据带宽。此外,功率效率要求非常高:每瓦高达10 Tera Operations Per Second (TOPS)。传统的计算体系结构不能很好地适应这些新需求。在此背景下,我们的目标是在器件的架构层面进行创新,通过考虑新兴技术,如硅光子学,它们在解决传统技术中的高功耗和低带宽问题方面的应用逐渐引起越来越多的关注。这些技术可以集成到拟议的特定应用架构中,通过利用硅光子学的低功耗和无与伦比的速度和带宽,实现CNN和RNN等深度学习算法。我们将定义一个架构探索流程,该流程支持基于光子的组件、分布式并行存储器和协调数据移动和计算的特定应用程序可编程处理器的集成。我们将开发集成上述组件的新性能模型。我们还将为深度学习算法提出高效的编程和部署模型。在这个模型中,安全性将是核心指标之一。本文的新颖性主要体现在:(1)基于硅光子学新兴技术的智能器件的架构级和系统级视角;(2)自上而下和自下而上相结合的方法;(3)与先进的物理级研究的完美互补,提出了创新的新兴技术。

项目成果

期刊论文数量(0)
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Nicolescu, Gabriela其他文献

Chip-Scale Silicon Photonic Interconnects: A Formal Study on Fabrication Non-Uniformity
  • DOI:
    10.1109/jlt.2016.2563781
  • 发表时间:
    2016-08-15
  • 期刊:
  • 影响因子:
    4.7
  • 作者:
    Nikdast, Mahdi;Nicolescu, Gabriela;Liboiron-Ladouceur, Odile
  • 通讯作者:
    Liboiron-Ladouceur, Odile
HyCo: A low-latency hybrid control plane for optical interconnection networks
HyCo:用于光互连网络的低延迟混合控制平面
  • DOI:
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Gohring, Felipe;Nikdast, Mahdi;Hessel, Fabiano;Libiron-Ladouceur, Odile;Nicolescu, Gabriela
  • 通讯作者:
    Nicolescu, Gabriela

Nicolescu, Gabriela的其他文献

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{{ truncateString('Nicolescu, Gabriela', 18)}}的其他基金

EDITH: Efficient Design for Intelligent devices exploiting emerging TecHnologies
EDITH:利用新兴技术的智能设备的高效设计
  • 批准号:
    RGPIN-2019-06965
  • 财政年份:
    2021
  • 资助金额:
    $ 2.4万
  • 项目类别:
    Discovery Grants Program - Individual
Mapping deep learning algorithms on systems-on chip
在片上系统上映射深度学习算法
  • 批准号:
    531142-2018
  • 财政年份:
    2021
  • 资助金额:
    $ 2.4万
  • 项目类别:
    Collaborative Research and Development Grants
Hardware and software interference mitigation for ARINC-653 compliant real-time operating systems on multi-core architectures
多核架构上符合 ARINC-653 标准的实时操作系统的硬件和软件干扰缓解
  • 批准号:
    538140-2018
  • 财政年份:
    2021
  • 资助金额:
    $ 2.4万
  • 项目类别:
    Collaborative Research and Development Grants
Mapping deep learning algorithms on systems-on chip
在片上系统上映射深度学习算法
  • 批准号:
    531142-2018
  • 财政年份:
    2020
  • 资助金额:
    $ 2.4万
  • 项目类别:
    Collaborative Research and Development Grants
EDITH: Efficient Design for Intelligent devices exploiting emerging TecHnologies
EDITH:利用新兴技术的智能设备的高效设计
  • 批准号:
    RGPIN-2019-06965
  • 财政年份:
    2020
  • 资助金额:
    $ 2.4万
  • 项目类别:
    Discovery Grants Program - Individual
Hardware and software interference mitigation for ARINC-653 compliant real-time operating systems on multi-core architectures
多核架构上符合 ARINC-653 标准的实时操作系统的硬件和软件干扰缓解
  • 批准号:
    538140-2018
  • 财政年份:
    2020
  • 资助金额:
    $ 2.4万
  • 项目类别:
    Collaborative Research and Development Grants
Mapping deep learning algorithms on systems-on chip
在片上系统上映射深度学习算法
  • 批准号:
    531142-2018
  • 财政年份:
    2019
  • 资助金额:
    $ 2.4万
  • 项目类别:
    Collaborative Research and Development Grants
Hardware and software interference mitigation for ARINC-653 compliant real-time operating systems on multi-core architectures
多核架构上符合 ARINC-653 标准的实时操作系统的硬件和软件干扰缓解
  • 批准号:
    538140-2018
  • 财政年份:
    2019
  • 资助金额:
    $ 2.4万
  • 项目类别:
    Collaborative Research and Development Grants
EDITH: Efficient Design for Intelligent devices exploiting emerging TecHnologies
EDITH:利用新兴技术的智能设备的高效设计
  • 批准号:
    RGPIN-2019-06965
  • 财政年份:
    2019
  • 资助金额:
    $ 2.4万
  • 项目类别:
    Discovery Grants Program - Individual
System-Level Modeling and Analysis of 3D Multi-Processors on Chip for Future Cloud Computing
面向未来云计算的片上 3D 多处理器的系统级建模和分析
  • 批准号:
    RGPIN-2014-03691
  • 财政年份:
    2018
  • 资助金额:
    $ 2.4万
  • 项目类别:
    Discovery Grants Program - Individual

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