Self-Sketching Domain Specific Accelerators: Build Hardware from Software

自绘制领域特定加速器：从软件构建硬件

• 批准号: RGPIN-2018-06795
• 负责人: Shriraman, Arrvindh
• 金额: $ 2.99万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=688358
• 关键词: Self; Sketching; Domain; Specific; Accelerators

While it is not always as apparent as open-source software and application innovation, computing hardware has been a key enabler for machine intelligence and AI, delivering 5000$\times$ performance improvement since 1987. Experts rank faster computing hardware as being nearly as important as neural networks for advancements in machine intelligence. Even more rapid growth in computing performance can fuel innovation in virtual reality, autonomous vehicles, and neural nets helping solve some of the grand challenges of the 21st century.***Unfortunately, the hardware industry faces a stiff challenge, today while we get more transistors on a single chip, chips themselves are more expensive (\$/$mm^2$) and transistors are energy inefficient; these limit performance. The lack of a clear forecast for technology and the rise of AI has entangled companies in the messy task of creating their own custom accelerator chips to deliver the requisite performance. Unfortunately, it requires significant effort (multiple years) and money (10s of million dollars) for developing the accelerator chip and software bring-up, inaccessible to anyone else but to the largest vendors. It is unclear how accelerator development can track the rapid pace of software and application evolution. ***My long-term goal is, how to develop systems to make up for the lack of technology scaling. Our goal is to accelerate system architecture innovation and make it sufficiently open and inexpensive that anyone (even small vendors) can build hardware anywhere. To achieve this goal and navigate the uncertain hardware customization landscape we propose to "build hardware from software". We will be developing open-source tools and compilers, which similar to generating an CPU binary, will now generate the specification for the hardware accelerator. The key novelty of our work is the observation that hardware design should separate application domain insights from the low-level implementation details, and that the domain insights can be captured from the data types in the program. This will enable our tools to generate hardware that is reusable, shareable across algorithms, optimizable using a tool, and can be tuned by a feedback-driven process. This work will fundamentally change how systems research is done. When CPUs and GPUs dominated the hardware landscape, software has assumed hardware to be a fixed design that cannot be changed. Our work leads application experts and software developers to question the status-quo and accelerates the movement towards customization.

虽然它并不总是像开源软件和应用程序创新那样明显，但计算硬件一直是机器智能和人工智能的关键推动者，自1987年以来提供了5000倍的性能改进。专家们认为，更快的计算硬件对于机器智能的进步几乎与神经网络一样重要。计算性能的快速增长可以推动虚拟现实、自动驾驶汽车和神经网络领域的创新，帮助解决世纪的一些重大挑战。不幸的是，硬件行业面临着严峻的挑战，今天，虽然我们在单个芯片上获得了更多的晶体管，但芯片本身更昂贵（$/$mm^2$），晶体管的能效更低;这些限制了性能。由于缺乏对技术的明确预测和人工智能的兴起，公司陷入了创建自己的定制加速器芯片以提供必要性能的混乱任务中。不幸的是，它需要大量的努力（多年）和资金（数千万美元）来开发加速器芯片和软件，除了最大的供应商之外，其他任何人都无法访问。目前还不清楚加速器开发如何跟踪软件和应用程序的快速发展。* 我的长期目标是，如何开发系统来弥补技术扩展的不足。我们的目标是加速系统架构创新，使其足够开放和廉价，任何人（即使是小供应商）都可以在任何地方构建硬件。为了实现这一目标，并在不确定的硬件定制环境中导航，我们建议“从软件构建硬件”。我们将开发开源工具和编译器，类似于生成CPU二进制文件，现在将生成硬件加速器的规范。我们工作的关键新奇是观察到硬件设计应该将应用领域的见解与低级别的实现细节分开，并且可以从程序中的数据类型中捕获领域见解。这将使我们的工具能够生成可重用的硬件，可跨算法共享，可使用工具优化，并且可以通过反馈驱动的过程进行调整。这项工作将从根本上改变系统研究的方式。当CPU和GPU主导硬件领域时，软件认为硬件是一个固定的设计，不能改变。我们的工作引导应用专家和软件开发人员质疑现状，并加速向定制化发展。