Hardware/Software Co-Design for Machine Learning Accelerators
机器学习加速器的硬件/软件协同设计
基本信息
- 批准号:RGPIN-2020-05889
- 负责人:
- 金额:$ 3.5万
- 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
- 来源:
- 关键词:
项目摘要
Artificial intelligence (AI) dominates our daily life, from automatic language translation to face detection. This success is due to the widespread adoption of artificial neural networks. These networks are inspired by the brain and solve tasks by letting the machine learn automatically, without requiring to be programmed beforehand. This machine learning approach has been known for over 30 years; however, up until now, there were simply not enough computational power available to handle real size problems. The widespread adoption of high performance parallel processors, such as Graphics Processing Units (GPUs), has enabled the development of complex neural networks able to solve real size problems. This has triggered the deep learning revolution, which is set to revolutionize virtually every field, from robotics to business. However, these new networks require devices capable of delivering even greater computational power, and more importantly, with high energy efficiency. Most of these workloads runs on the cloud or on Internet's edge (e.g. mobile devices) where energy consumption is the prime concern. As a result, a new race among companies has started to design the most energy efficient accelerators specialized for machine learning. The development of new hardware is a very time consuming, multi-years, process. This involves a lot of manual design and testing requiring large teams of designers. Furthermore, hardware companies tend to take a conservative approach and design their new hardware for well known, well established machine learning techniques. This hinders the adoption of new machine learning techniques since hardware is always designed for obsolete techniques. The long term goal of this proposal is to develop a set of common principles for fully automating the design of accelerators, with the short term five years goal focusing on neural networks in particular. This involves both automating hardware design as well as software interface design, known in the community as co-design. When a new accelerator emerges, it can simply be dropped in as a replacement for the older one and will just work out of the box. This will also lead to a rapid turnaround time from design to hardware, which ultimately means more accelerator diversity, each specialized for a particular type of problems. This will enable the adoption of new revolutionary machine learning applications that might otherwise never see daylight. Canada is considered by many as the leader for machine learning and its researchers have pioneered many advances in the field. Given the importance that specialized hardware plays in enabling machine learning applications, this research program will strengthen this position on the system side by training the next generation of system-focused machine learning expert. We intend to release all our software as open source, to encourage uptake of our techniques among researchers and industry players.
人工智能(AI)主导着我们的日常生活,从自动语言翻译到人脸检测。这一成功归功于人工神经网络的广泛采用。这些网络受到大脑的启发,通过让机器自动学习来解决任务,而不需要事先编程。这种机器学习方法已经存在了30多年;然而,到目前为止,根本没有足够的计算能力来处理真实的大小问题。诸如图形处理单元(GPU)的高性能并行处理器的广泛采用使得能够开发能够解决真实的尺寸问题的复杂神经网络。这引发了深度学习革命,这将彻底改变从机器人到商业的几乎所有领域。然而,这些新网络需要能够提供更大计算能力的设备,更重要的是,具有高能效。这些工作负载中的大多数在云或互联网边缘(例如移动的设备)上运行,其中能耗是主要问题。因此,公司之间开始了一场新的竞赛,设计专门用于机器学习的最节能的加速器。新硬件的开发是一个非常耗时、多年的过程。这涉及到大量的手动设计和测试,需要大量的设计师团队。此外,硬件公司倾向于采取保守的方法,并为众所周知的、成熟的机器学习技术设计新硬件。这阻碍了新机器学习技术的采用,因为硬件总是为过时的技术而设计。该提案的长期目标是制定一套加速器设计完全自动化的共同原则,短期五年目标特别关注神经网络。这涉及自动化硬件设计以及软件接口设计,在社区中称为协同设计。当一个新的加速器出现时,它可以简单地作为旧加速器的替代品,并且可以开箱即用。这也将导致从设计到硬件的快速周转时间,这最终意味着更多的加速器多样性,每个加速器专门用于特定类型的问题。这将使人们能够采用新的革命性机器学习应用程序,否则这些应用程序可能永远不会看到日光。加拿大被许多人认为是机器学习的领导者,其研究人员在该领域取得了许多进展。鉴于专用硬件在实现机器学习应用方面的重要性,该研究计划将通过培训下一代以系统为中心的机器学习专家来加强系统方面的地位。我们打算将我们所有的软件作为开源发布,以鼓励研究人员和行业参与者采用我们的技术。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Dubach, Christophe其他文献
Dubach, Christophe的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Dubach, Christophe', 18)}}的其他基金
Hardware/Software Co-Design for Machine Learning Accelerators
机器学习加速器的硬件/软件协同设计
- 批准号:
RGPIN-2020-05889 - 财政年份:2021
- 资助金额:
$ 3.5万 - 项目类别:
Discovery Grants Program - Individual
Hardware/Software Co-Design for Machine Learning Accelerators
机器学习加速器的硬件/软件协同设计
- 批准号:
RGPIN-2020-05889 - 财政年份:2020
- 资助金额:
$ 3.5万 - 项目类别:
Discovery Grants Program - Individual
相似海外基金
SHF: Small: Taming Huge Page Problems for Memory Bulk Operations Using a Hardware/Software Co-Design Approach
SHF:小:使用硬件/软件协同设计方法解决内存批量操作的大页面问题
- 批准号:
2400014 - 财政年份:2024
- 资助金额:
$ 3.5万 - 项目类别:
Standard Grant
SHF: Small: Hardware-Software Co-design for Privacy Protection on Deep Learning-based Recommendation Systems
SHF:小型:基于深度学习的推荐系统的隐私保护软硬件协同设计
- 批准号:
2334628 - 财政年份:2024
- 资助金额:
$ 3.5万 - 项目类别:
Standard Grant
CAREER: Enabling Scalable and Resilient Quantum Computer Architectures through Synergistic Hardware-Software Co-Design
职业:通过协同硬件软件协同设计实现可扩展且有弹性的量子计算机架构
- 批准号:
2340267 - 财政年份:2024
- 资助金额:
$ 3.5万 - 项目类别:
Continuing Grant
Conference: NSF Workshop on Hardware-Software Co-design for Neuro-Symbolic Computation
会议:NSF 神经符号计算软硬件协同设计研讨会
- 批准号:
2338640 - 财政年份:2023
- 资助金额:
$ 3.5万 - 项目类别:
Standard Grant
POSE: Phase II: Building an Open-Source Ecosystem for Deep-Learning Hardware-Software Co-Design
POSE:第二阶段:构建深度学习软硬件协同设计的开源生态系统
- 批准号:
2303735 - 财政年份:2023
- 资助金额:
$ 3.5万 - 项目类别:
Standard Grant
Collaborative Research: CSR: Small: Expediting Continual Online Learning on Edge Platforms through Software-Hardware Co-designs
协作研究:企业社会责任:小型:通过软硬件协同设计加快边缘平台上的持续在线学习
- 批准号:
2312157 - 财政年份:2023
- 资助金额:
$ 3.5万 - 项目类别:
Standard Grant
Collaborative Research: SHF: Medium: A hardware-software co-design approach for high-performance in-memory analytic data processing
协作研究:SHF:中:用于高性能内存分析数据处理的硬件软件协同设计方法
- 批准号:
2312741 - 财政年份:2023
- 资助金额:
$ 3.5万 - 项目类别:
Standard Grant
Collaborative Research: SHF: Medium: A hardware-software co-design approach for high-performance in-memory analytic data processing
协作研究:SHF:中:用于高性能内存分析数据处理的硬件软件协同设计方法
- 批准号:
2312739 - 财政年份:2023
- 资助金额:
$ 3.5万 - 项目类别:
Standard Grant
SHF: Medium: Efficient and Scalable Pattern Matching via Hardware-Software Co-Design
SHF:中:通过软硬件协同设计实现高效且可扩展的模式匹配
- 批准号:
2313062 - 财政年份:2023
- 资助金额:
$ 3.5万 - 项目类别:
Continuing Grant
SHF: Small: Improving Efficiency of Vision Transformers via Software-Hardware Co-Design and Acceleration
SHF:小型:通过软硬件协同设计和加速提高视觉变压器的效率
- 批准号:
2233893 - 财政年份:2023
- 资助金额:
$ 3.5万 - 项目类别:
Standard Grant