90 citations · 104 across the 4 of their papers we have counts for
6 papers
Efficient N:M Sparse DNN Training Using Algorithm, Architecture, and Dataflow Co-Design
Chao Fang, Wei Sun, Aojun Zhou +1
Sparse training is one of the promising techniques to reduce the computational cost of DNNs while retaining high accuracy. In particular, N:M fine-grained structured sparsity, wher…
S2R: Exploring a Double-Win Transformer-Based Framework for Ideal and Blind Super-Resolution
Minghao She, Wendong Mao, Huihong Shi +1
Nowadays, deep learning based methods have demonstrated impressive performance on ideal super-resolution (SR) datasets, but most of these methods incur dramatically performance dro…
An Algorithm-Hardware Co-Optimized Framework for Accelerating N:M Sparse Transformers
Chao Fang, Aojun Zhou, Zhongfeng Wang
The Transformer has been an indispensable staple in deep learning. However, for real-life applications, it is very challenging to deploy efficient Transformers due to immense param…
GANDSE: Generative Adversarial Network based Design Space Exploration for Neural Network Accelerator Design
Lang Feng, Wenjian Liu, Chuliang Guo +3
With the popularity of deep learning, the hardware implementation platform of deep learning has received increasing interest. Unlike the general purpose devices, e.g., CPU, or GPU,…
Learning Robust and Lightweight Model through Separable Structured Transformations
Xian Wei, Yanhui Huang, Yangyu Xu +5
With the proliferation of mobile devices and the Internet of Things, deep learning models are increasingly deployed on devices with limited computing resources and memory, and are…
Intra-layer Nonuniform Quantization for Deep Convolutional Neural Network
Fangxuan Sun, Jun Lin, Zhongfeng Wang
Deep convolutional neural network (DCNN) has achieved remarkable performance on object detection and speech recognition in recent years. However, the excellent performance of a DCN…