1 citations · 1 across the 2 of their papers we have counts for
3 papers
Learn to See Faster: Pushing the Limits of High-Speed Camera with Deep Underexposed Image Denoising
Weihao Zhuang, Tristan Hascoet, Ryoichi Takashima +1
The ability to record high-fidelity videos at high acquisition rates is central to the study of fast moving phenomena. The difficulty of imaging fast moving scenes lies in a trade-…
Memory-Efficient CNN Accelerator Based on Interlayer Feature Map Compression
Zhuang Shao, Xiaoliang Chen, Li Du +6
Existing deep convolutional neural networks (CNNs) generate massive interlayer feature data during network inference. To maintain real-time processing in embedded systems, large on…
Reversible designs for extreme memory cost reduction of CNN training
Tristan Hascoet, Quentin Febvre, Yasuo Ariki +1
Training Convolutional Neural Networks (CNN) is a resource intensive task that requires specialized hardware for efficient computation. One of the most limiting bottleneck of CNN t…