面向嵌入式计算系统探索CNN中硬件友好的瓶颈架构
Exploring Hardware Friendly Bottleneck Architecture in CNN for Embedded Computing Systems
AI总结：
本文针对嵌入式计算系统提出硬件友好的轻量级CNN模型L-Mobilenet，结合前沿网络结构设计与软硬件协同优化，在ZYNQ平台上实现了精度相当前提下的速度与参数量显著提升。
摘要：
本文探索了如何为嵌入式计算系统设计轻量级CNN架构。我们针对基于ZYNQ的硬件平台提出了L-Mobilenet模型。L-Mobilenet能够很好地适配硬件计算与加速，其网络结构受Inception-ResnetV1和MobilenetV2这两项前沿工作的启发，可在保持推理精度的同时有效减少参数量与延迟。我们将L-Mobilenet模型部署到ZYNQ嵌入式平台，以全面评估所提设计的性能。通过在cifar10和cifar100数据集上的测试，L-Mobilenet模型在保持相近精度的情况下，速度可达MobileNetV2的3倍，参数量仅为MobileNetV2的1/3.7；在保持同等精度的情况下，速度可达ShufflenetV2的2倍，参数量仅为ShufflenetV2的1/1.5。实验表明，由于我们在L-Mobilenet瓶颈架构中对硬件加速和软硬件协同设计策略进行了特殊考量，该网络模型能够取得更优的性能。
英文摘要：
In this paper, we explore how to design lightweight CNN architecture for embedded computing systems. We propose L-Mobilenet model for ZYNQ based hardware platform. L-Mobilenet can adapt well to the hardware computing and accelerating, and its network structure is inspired by the state-of-the-art work of Inception-ResnetV1 and MobilenetV2, which can effectively reduce parameters and delay while maintaining the accuracy of inference. We deploy our L-Mobilenet model to ZYNQ embedded platform for fully evaluating the performance of our design. By measuring in cifar10 and cifar100 datasets, L-Mobilenet model is able to gain 3x speed up and 3.7x fewer parameters than MobileNetV2 while maintaining a similar accuracy. It also can obtain 2x speed up and 1.5x fewer parameters than ShufflenetV2 while maintaining the same accuracy. Experiments show that our network model can obtain better performance because of the special considerations for hardware accelerating and software-hardware co-design strategies in our L-Mobilenet bottleneck architecture.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
