Self-Sketching Domain Specific Accelerators: Build Hardware from Software

虽然它并不总是像开源软件和应用创新那样明显，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，它的性能提高了5000倍。专家认为，对于机器智能的进步，更快的计算硬件几乎和神经网络一样重要。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的创新，帮助解决21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，当我们在单个芯片上获得更多晶体管时，芯片本身更昂贵（\$/$mm^2$），晶体管的能源效率低下；这些限制了性能。由于缺乏对技术的明确预测以及人工智能的兴起，企业陷入了制造自己的定制加速器芯片以提供必要性能的艰巨任务。不幸的是，开发加速器芯片和软件需要大量的努力（多年）和资金（数千万美元），除了最大的供应商，其他人都无法获得。目前尚不清楚加速器开发如何能够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规模的不足。我们的目标是加速系统架构创新，并使其足够开放和廉价，任何人（甚至是小供应商）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，这与生成CPU二进制文件类似，现在将生成硬件加速器的规范。我们工作的关键新颖之处在于观察到硬件设计应该将应用程序领域洞察从底层实现细节中分离出来，并且领域洞察可以从程序中的数据类型中获取。这将使我们的工具能够生成可重用的、可跨算法共享的、可使用工具优化的硬件，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在硬件领域占据主导地位时，软件认为硬件是一种无法改变的固定设计。我们的工作引导应用程序专家和软件开发人员质疑现状，并加速向定制化的转变。

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.