54 citations · 135 across the 17 of their papers we have counts for
6 papers · 1 filter
AutoDistill: an End-to-End Framework to Explore and Distill Hardware-Efficient Language Models
Xiaofan Zhang, Zongwei Zhou, Deming Chen +1
Recently, large pre-trained models have significantly improved the performance of various Natural LanguageProcessing (NLP) tasks but they are expensive to serve due to long serving…
Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture
Seung Won Min, Kun Wu, Sitao Huang +5
Graph Convolutional Networks (GCNs) are increasingly adopted in large-scale graph-based recommender systems. Training GCN requires the minibatch generator traversing graphs and sam…
PyTorch-Direct: Enabling GPU Centric Data Access for Very Large Graph Neural Network Training with Irregular Accesses
Seung Won Min, Kun Wu, Sitao Huang +5
With the increasing adoption of graph neural networks (GNNs) in the machine learning community, GPUs have become an essential tool to accelerate GNN training. However, training GNN…
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…
EDD: Efficient Differentiable DNN Architecture and Implementation Co-search for Embedded AI Solutions
Yuhong Li, Cong Hao, Xiaofan Zhang +5
High quality AI solutions require joint optimization of AI algorithms and their hardware implementations. In this work, we are the first to propose a fully simultaneous, efficient…
NAIS: Neural Architecture and Implementation Search and its Applications in Autonomous Driving
Cong Hao, Yao Chen, Xinheng Liu +9
The rapidly growing demands for powerful AI algorithms in many application domains have motivated massive investment in both high-quality deep neural network (DNN) models and high-…