140 citations · 719 across the 50 of their papers we have counts for
50 papers
Block-Wise Dynamic-Precision Neural Network Training Acceleration via Online Quantization Sensitivity Analytics
Ruoyang Liu, Chenhan Wei, Yixiong Yang +3
Data quantization is an effective method to accelerate neural network training and reduce power consumption. However, it is challenging to perform low-bit quantized training: the c…
Cross-layer Attention Network for Fine-grained Visual Categorization
Ranran Huang, Yu Wang, Huazhong Yang
Learning discriminative representations for subtle localized details plays a significant role in Fine-grained Visual Categorization (FGVC). Compared to previous attention-based wor…
GRAPHIC: GatheR-And-Process in Highly parallel with In-SSD Compression Architecture in Very Large-Scale Graph
Yiming Chen, Guohao Dai, Mufeng Zhou +8
Graph convolutional network (GCN), an emerging algorithm for graph computing, has achieved promising performance in graphstructure tasks. To achieve acceleration for data-intensive…
YOLoC: DeploY Large-Scale Neural Network by ROM-based Computing-in-Memory using ResiduaL Branch on a Chip
Yiming Chen, Guodong Yin, Zhanhong Tan +6
Computing-in-memory (CiM) is a promising technique to achieve high energy efficiency in data-intensive matrix-vector multiplication (MVM) by relieving the memory bottleneck. Unfort…
FAST: A Fully-Concurrent Access Technique to All SRAM Rows for Enhanced Speed and Energy Efficiency in Data-Intensive Applications
Yiming Chen, Yushen Fu, Mingyen Lee +5
Compute-in-memory (CiM) is a promising approach to improving the computing speed and energy efficiency in dataintensive applications. Beyond existing CiM techniques of bitwise logi…
Explore-Bench: Data Sets, Metrics and Evaluations for Frontier-based and Deep-reinforcement-learning-based Autonomous Exploration
Yuanfan Xu, Jincheng Yu, Jiahao Tang +5
Autonomous exploration and mapping of unknown terrains employing single or multiple robots is an essential task in mobile robotics and has therefore been widely investigated. Never…