论文标题
Hybriddnn：高性能混合DNN加速器设计和实现的框架
HybridDNN: A Framework for High-Performance Hybrid DNN Accelerator Design and Implementation
论文作者
论文摘要
为了加快深层神经网络（DNN）加速器的设计并启用有效的实现，我们提出了Hybriddnn，这是一个用于构建高性能混合DNN加速器并提供基于FPGA的硬件实现的框架。新颖的技术包括具有混合空间/Winograd卷积（CORV）处理引擎（PE）的高度灵活和可扩展的体系结构，一种全面的设计空间探索工具，以及完整的设计流程，以完全支持加速器的设计和实现。实验结果表明，Hybriddnn生成的加速器可以在高端FPGA（VU9P）和嵌入式FPGA（PYNQ-Z1）上提供3375.7和83.3 GOP，与州立Act Accelerator设计相比，该嵌入式FPGA（PYNQ-Z1）可以提高1.8倍的性能。这表明Hybriddnn具有灵活性和可扩展性，并且可以针对具有截然不同的资源约束的云和嵌入式硬件平台。
To speedup Deep Neural Networks (DNN) accelerator design and enable effective implementation, we propose HybridDNN, a framework for building high-performance hybrid DNN accelerators and delivering FPGA-based hardware implementations. Novel techniques include a highly flexible and scalable architecture with a hybrid Spatial/Winograd convolution (CONV) Processing Engine (PE), a comprehensive design space exploration tool, and a complete design flow to fully support accelerator design and implementation. Experimental results show that the accelerators generated by HybridDNN can deliver 3375.7 and 83.3 GOPS on a high-end FPGA (VU9P) and an embedded FPGA (PYNQ-Z1), respectively, which achieve a 1.8x higher performance improvement compared to the state-of-art accelerator designs. This demonstrates that HybridDNN is flexible and scalable and can target both cloud and embedded hardware platforms with vastly different resource constraints.
