140 citations · 719 across the 50 of their papers we have counts for
9 papers · 1 filter
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…
Heuristic Adaptability to Input Dynamics for SpMM on GPUs
Guohao Dai, Guyue Huang, Shang Yang +6
Sparse Matrix-Matrix Multiplication (SpMM) has served as fundamental components in various domains. Many previous studies exploit GPUs for SpMM acceleration because GPUs provide hi…
High Area/Energy Efficiency RRAM CNN Accelerator with Kernel-Reordering Weight Mapping Scheme Based on Pattern Pruning
Songming Yu, Yongpan Liu, Lu Zhang +5
Resistive Random Access Memory (RRAM) is an emerging device for processing-in-memory (PIM) architecture to accelerate convolutional neural network (CNN). However, due to the highly…
A Survey of FPGA-Based Neural Network Accelerator
Kaiyuan Guo, Shulin Zeng, Jincheng Yu +2
Recent researches on neural network have shown significant advantage in machine learning over traditional algorithms based on handcrafted features and models. Neural network is now…