@article {369,
	title = {A GPU-Outperforming FPGA Accelerator Architecture for Binary Convolutional Neural Networks},
	journal = {ACM Journal on Emerging Technologies in Computing (JETC) - Special Issue on Frontiers of Hardware and Algorithms for On-chip Learning​},
	volume = {14},
	year = {2018},
	month = {07/2018},
	pages = {18.16},
	chapter = {18.1},
	abstract = {<p><span style="color:rgb(42, 42, 42); font-family:Roboto, \&quot;Helvetica Neue\&quot;, Helvetica, Arial, sans-serif; font-size:20px; background-color:rgb(245, 245, 245)">FPGA-based hardware accelerators for convolutional neural networks (CNNs) have obtained great attentions due to their higher energy efficiency than GPUs. However, it is challenging for FPGA-based solutions to achieve a higher throughput than GPU counterparts. In this paper, we demonstrate that FPGA acceleration can be a superior solution in terms of both throughput and energy efficiency when a CNN is trained with binary constraints on weights and activations. Specifically, we propose an optimized FPGA accelerator architecture tailored for bitwise convolution and normalization that features massive spatial parallelism with deep pipelines stages. A key advantage of the FPGA accelerator is that its performance is insensitive to data batch size, while the performance of GPU acceleration varies largely depending on the batch size of the data. Experiment results show that the proposed accelerator architecture for binary CNNs running on a Virtex-7 FPGA is 8.3x faster and 75x more energy-efficient than a Titan X GPU for processing online individual requests in small batch sizes. For processing static data in large batch sizes, the proposed solution is on a par with a Titan X GPU in terms of throughput while delivering 9.5x higher energy efficiency.</span></p>
},
	keywords = {psclab},
	doi = {10.1145/3154839},
	url = {https://dl.acm.org/citation.cfm?id=3154839},
	author = {YIxing Li and Zichuan Liu and Kai Xu and Hao Yu and Fengbo Ren}
}
@inproceedings {szegedy2015going,
	title = {Going deeper with convolutions},
	year = {2015},
	pages = {1{\textendash}9},
	keywords = {Deep Learning, ref},
	author = {Szegedy, Christian and Liu, Wei and Jia, Yangqing and Sermanet, Pierre and Reed, Scott and Anguelov, Dragomir and Erhan, Dumitru and Vanhoucke, Vincent and Rabinovich, Andrew}
}
@article {figueiredo2007gradient,
	title = {Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems},
	journal = {IEEE Journal of selected topics in signal processing},
	volume = {1},
	number = {4},
	year = {2007},
	pages = {586{\textendash}597},
	publisher = {IEEE},
	keywords = {Compressive Sensing, ref},
	author = {Figueiredo, M{\'a}rio AT and Nowak, Robert D and Wright, Stephen J}
}
