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使用基于高水平合成的硬件生成器进行神经网络加速器准确基准测试的方法
Kartik Prabhu1, Jeffrey Yu1, Xinyuan Allen Pan1
1Stanford University , Stanford, CA, USA.
概括
基于不同设计,对神经网络加速器进行基准测试具有挑战性. 我们介绍了Voyager,这是一个通过创建匹配的基线设计进行系统评估来公平地比较硬件加速器的工具.
科学领域:
- 计算机工程 计算机工程
- 人工智能 硬件 硬件
背景情况:
- 神经网络模型的复杂性正在增加,推动对专门硬件加速器的需求.
- 由于技术节点和工作负载变化等外部因素,现有的评估指标 (例如TOPS,TOPS/W) 不足以进行公平的比较.
研究的目的:
- 提出一种标准化的方法来对神经网络加速器进行基准测试.
- 为了在各种加速器架构中实现公平的,果对果的比较.
主要方法:
- 开发了"旅行者",一个基于高水平合成 (HLS) 的加速器发电机.
- 创建了基线加速器与匹配的计算规模和技术节点.
- 在生成的加速器上运行相同的工作负载以进行比较分析.
主要成果:
- 旅行者使得能够创建优化的基线加速器.
- 该方法方便对加速器架构进行系统评估.
- 案例研究表明,在不同的加速器类型之间进行有效的比较,包括内存和稀疏性意识的设计.
结论:
- 使用Voyager的拟议基准测试方法为评估神经网络加速器提供了一个强大的框架.
- 这种方法可以隔离建筑影响,从而产生更有意义的分析和设计见解.
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