76 citations · 92 across the 4 of their papers we have counts for
4 papers
Understanding the Potential of FPGA-Based Spatial Acceleration for Large Language Model Inference
Hongzheng Chen, Jiahao Zhang, Yixiao Du +5
Recent advancements in large language models (LLMs) boasting billions of parameters have generated a significant demand for efficient deployment in inference workloads. The majorit…
Slapo: A Schedule Language for Progressive Optimization of Large Deep Learning Model Training
Hongzheng Chen, Cody Hao Yu, Shuai Zheng +3
Recent years have seen an increase in the development of large deep learning (DL) models, which makes training efficiency crucial. Common practice is struggling with the trade-off…
Structured Pruning is All You Need for Pruning CNNs at Initialization
Yaohui Cai, Weizhe Hua, Hongzheng Chen +3
Pruning is a popular technique for reducing the model size and computational cost of convolutional neural networks (CNNs). However, a slow retraining or fine-tuning procedure is of…
FracBNN: Accurate and FPGA-Efficient Binary Neural Networks with Fractional Activations
Yichi Zhang, Junhao Pan, Xinheng Liu +3
Binary neural networks (BNNs) have 1-bit weights and activations. Such networks are well suited for FPGAs, as their dominant computations are bitwise arithmetic and the memory requ…