3 citations · 3 across the 4 of their papers we have counts for
4 papers
A Dense Tensor Accelerator with Data Exchange Mesh for DNN and Vision Workloads
Yu-Sheng Lin, Wei-Chao Chen. Chia-Lin Yang, Shao-Yi Chien
We propose a dense tensor accelerator called VectorMesh, a scalable, memory-efficient architecture that can support a wide variety of DNN and computer vision workloads. Its buildin…
GrateTile: Efficient Sparse Tensor Tiling for CNN Processing
Yu-Sheng Lin, Hung Chang Lu, Yang-Bin Tsao +3
We propose GrateTile, an efficient, hardwarefriendly data storage scheme for sparse CNN feature maps (activations). It divides data into uneven-sized subtensors and, with small ind…
MERIT: Tensor Transform for Memory-Efficient Vision Processing on Parallel Architectures
Yu-Sheng Lin, Wei-Chao Chen, Shao-Yi Chien
Computationally intensive deep neural networks (DNNs) are well-suited to run on GPUs, but newly developed algorithms usually require the heavily optimized DNN routines to work effi…
Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel Removal
Chih-Ting Liu, Yi-Heng Wu, Yu-Sheng Lin +1
Deep Convolutional Neural Networks (CNNs) are widely employed in modern computer vision algorithms, where the input image is convolved iteratively by many kernels to extract the kn…