Scaling extreme analYtics with Cross-architecture acceleration based on OPen Standards

人工智能和分析的广泛采用导致新型硬件加速器市场迅速扩大，这些加速器可以在云和边缘提供高效节能的训练和推理任务扩展。不幸的是，当今所有流行的解决方案人工智能加速解决方案都使用专有的、封闭的硬件软件堆栈，导致人工智能加速市场被少数大型行业参与者垄断。SYCLOPS项目的愿景是通过使用开放标准实现人工智能加速的民主化，为欧洲及其他地区提供更好的人工智能/数据挖掘解决方案，并为欧洲及其他地区提供健康，有竞争力，创新驱动的生态系统。这一愿景依赖于行业两个重要趋势的融合：（i）RISCV的标准化和采用，一种免费的开放式指令集架构（伊萨），用于人工智能和分析加速，以及（ii）SYCL作为所有类型加速器的跨供应商，跨架构，数据并行编程模型的出现和发展，SYCLOPS项目的目标是首次将这些标准结合在一起，以便（i）使用基于标准的，完全开放的，人工智能加速方法和（ii）实现可互操作（开放和供应商中立的接口/API），可信赖（可验证和基于标准的硬件/软件）和绿色（通过特定于应用的处理器定制）人工智能系统的开发。在此过程中，我们将利用在SYCLOPS中获得的经验为SYCL和RISC-V标准做出贡献，并促进与各自学术，工业和创新社区（RISC-V基金会，EPI，Khronos，ISO C++）的联系。将这两个标准结合在一起，可以在两个标准中进行协同设计，这反过来将使AI加速器的设计空间更广阔，并形成更丰富的解决方案生态系统。

The wide-spread adoption of AI and analytics has resulted in a rapidly expanding market for novel hardware accelerators that can provide energy-efficient scaling of training and inference tasks at both the cloud and edge. Unfortunately, all popular solutions AI acceleration solutions today use proprietary, closed hardware—software stacks, leading to a monopolization of the AI acceleration market by a few large industry players. The vision of SYCLOPS project is to enable better solutions for AI/data mining for extremely large and diverse data by democratizing AI acceleration using open standards, and enabling a healthy, competitive, innovation-driven ecosystem for Europe and beyond. This vision relies on the convergence of two important trends in the industry: (i) the standardization and adoption of RISCV, a free, open Instruction Set Architecture (ISA), for AI and analytics acceleration, and (ii) the emergence and growth of SYCL as a cross-vendor, cross-architecture, data parallel programming model for all types of accelerators, including RISC-V. The goal of project SYCLOPS is to bring together these standards for the first time in order to (i) demonstrate ground-breaking advances in performance and scalability of extreme data analytics using a standards-based, fully-open, AI acceleration approach and (ii) enable the development of inter-operable (open and vendor neutral interfaces/APIs), trustworthy (verifiable and standards-based hardware/software), and green (via application-specific processor customization) AI systems. In doing so, we will use the experience gained in SYCLOPS to contribute back to SYCL and RISC-V standards and foster links to respective academic, industrial and innovator communities (RISC-V foundation, EPI, Khronos, ISO C++). Bringing together the two standards enables codesign in both standards, which in turn, will enable a broader AI accelerator design space, and a richer ecosystem of solutions.