HCG:流式DCNN加速器与FPGA上的混合计算颗粒度方案
概括
本研究介绍了一种混合计算颗粒度 (HCG) 方案,用于在FPGA上传输DCNN加速器. HCG优化了内存带宽权衡和FPGA资源使用,提高了深度学习任务的吞吐量.
科学领域:
- 计算机工程 计算机工程
- 硬件加速器 硬件加速器
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 在FPGA上的流式深层卷积神经网络 (DCNN) 加速器利用卷积层间并行来提高吞吐量.
- 现有的加速器经常使用统一的制方法,影响内存带宽权衡和FPGA资源利用.
- 计算颗粒度,输出特征图的大小计算每输入特征图,是影响性能的一个关键因素.
研究的目的:
- 引入一个新的混合计算粒度 (HCG) 方案,用于流式DCNN加速器.
- 分析HCG对系统管道的影响,旨在消除管道泡并简化设计.
- 开发一个硬件开销模型和启发式算法来优化HCG并实现更好的内存带宽权衡和吞吐量.
主要方法:
- 开发了一个混合计算颗粒度 (HCG) 方案,以独立优化每个节点的计算颗粒度.
- 理论上分析了计算颗粒度对系统管道和控制逻辑的影响.
- 创建了一个硬件上部模型和启发式算法,以优化颗粒度.
- 在各种带宽限制下,在Alveo U250板上实现并验证了加速器.
主要成果:
- HCG方案使得更灵活的内存带宽权衡和有效的FPGA资源利用成为可能.
- 提出的方法成功建立了无系统管道,没有气泡,简化了设计.
- 实施的加速器实现了高吞吐量 (例如,3-TOPS ResNet-18),在特定的带宽限制下超过了现有工作.
- 为各种DCNN模型展示了加速器,包括VGG-16,ResNet-34/50,MobileNetV1,ConvNeXt-T和ResNeXt-50. 这些加速器都在使用中.
结论:
- HCG方案是优化流媒体DCNN加速器在FPGA上的有效方法.
- 开发的优化方法实现了卓越的内存带宽权衡和更高的吞吐量.
- 提出的设计和验证方法表明,基于FPGA的深度学习加速取得了重大进展.
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