Accelergy: An Architecture-Level Energy Estimation Methodology for Accelerator Designs
Accelergy: An Architecture-Level Energy Estimation Methodology for Accelerator Designs
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DOI：
10.1109/iccad45719.2019.8942149
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发表时间：
2019-11
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影响因子：
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通讯作者：
Yannan Nellie Wu;J. Emer;V. Sze
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作者：
Yannan Nellie Wu;J. Emer;V. Sze
随着摩尔定律放缓以及登纳德缩放定律失效，高能效的特定领域加速器，例如用于机器学习的深度神经网络（DNN）处理器以及用于云应用的可编程网络交换机，已成为硬件设计师继续为数据和计算密集型应用提高能效的一种有前景的方式。为确保快速探索加速器设计空间，在不需要设计的完整硬件描述的情况下进行能量估算的架构级能量估算器对设计师至关重要。然而，由于加速器设计多样且对数据模式敏感，使用现有的架构级能量估算器来获得加速器设计的准确估算很困难。本文提出了Accelergy，一种适用于加速器的通用能量估算方法，它允许设计规范由用户定义的高级复合组件和用户定义的低级原始组件组成，这些组件可通过第三方能量估算插件来表征。还提供了一个用于DNN加速器设计的原始组件和复合组件的示例，作为所提出方法的应用。总体而言，Accelergy在著名的DNN加速器设计Eyeriss上达到了95%的准确率，并且能够正确地获取不同粒度组件的能量分解。Accelergy代码可在http://accelergy.mit.edu获取。
With Moore's law slowing down and Dennard scaling ended, energy-efficient domain-specific accelerators, such as deep neural network (DNN) processors for machine learning and programmable network switches for cloud applications, have become a promising way for hardware designers to continue bringing energy efficiency improvements to data and computation-intensive applications. To ensure the fast exploration of the accelerator design space, architecture-level energy estimators, which perform energy estimations without requiring complete hardware description of the designs, are critical to designers. However, it is difficult to use existing architecture-level energy estimators to obtain accurate estimates for accelerator designs, as accelerator designs are diverse and sensitive to data patterns. This paper presents Accelergy, a generally applicable energy estimation methodology for accelerators that allows design specifications comprised of user-defined high-level compound components and user-defined low-level primitive components, which can be characterized by third-party energy estimation plug-ins. An example with primitive and compound components for DNN accelerator designs is also provided as an application of the proposed methodology. Overall, Accelergy achieves 95% accuracy on Eyeriss, a well-known DNN accelerator design, and can correctly capture the energy breakdown of components at different granularities. The Accelergy code is available at http://accelergy.mit.edu.