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Co-Optimizing CPUs and Accelerators in Constrained Systems

机译：在约束系统中共同优化CPU和加速器

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摘要

The rapid rise in popularity of machine learning techniques to mimic functionalities of human cognition and solve problems such as natural language processing has driven a push toward the creation of application-specific accelerators included alongside general-purpose CPUs to improve the performance of inference applications. While significant work has been done to model these accelerators and create ways to explore their design spaces and have even incorporated external effects like data transfer latency, they do not account for the portion of the workload running on the CPU. When designing an electronic system under constraints, prioritizing the accelerator can harm the power or performance of the CPU and reduce the overall quality of the design when running workloads with significant components on it. In this work, we present several workloads running on a RISC- V system with an accelerator tailored to each one and show how the overall power, performance, and area can benefit in the presence of constraints by co-designing the two parts. By using this methodology, we show that power, performance, and area can be improved by up to 66%, 40%, and 25 %, respectively, given constraints on each metric.
机译：迅速崛起的机器学习技术的普及，人类认知的模拟功能和解决的问题，如自然语言处理，带动朝着创建包含旁边通用CPU来提高推理应用程序的性能特定应用加速器的推动。虽然显著的工作已经完成，这些加速器模型和创建方式探索他们的设计空间，甚至纳入像数据传输延迟的外部影响，他们不占CPU上运行的工作负载的一部分。当设计约束条件下的电子系统，优先加速器会损害CPU的功率和性能，并与它显著组件上运行工作负载时降低了设计的整体质量。在这项工作中，我们提出几个工作负载适合每个人，并展示如何在整体功耗，性能和面积可以限制的情况下通过联合设计两个部分受益用于加速器的RISC精简指令影音播放系统上运行。通过使用这种方法，我们表明，功率，性能和面积可通过分别高达66％，40％和25％，对每个度量给定约束得到改善。

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