18 citations · 18 across the 1 of their papers we have counts for
3 papers
cs.DC2020★ 18 cited
Efficient Execution of Quantized Deep Learning Models: A Compiler Approach
Animesh Jain, Shoubhik Bhattacharya, Masahiro Masuda +2
A growing number of applications implement predictive functions using deep learning models, which require heavy use of compute and memory. One popular technique for increasing reso…
cs.PL2020
Nimble: Efficiently Compiling Dynamic Neural Networks for Model Inference
Haichen Shen, Jared Roesch, Zhi Chen +6
Modern deep neural networks increasingly make use of features such as dynamic control flow, data structures and dynamic tensor shapes. Existing deep learning systems focus on optim…
cs.DC2018
Optimizing CNN Model Inference on CPUs
Yizhi Liu, Yao Wang, Ruofei Yu +3
The popularity of Convolutional Neural Network (CNN) models and the ubiquity of CPUs imply that better performance of CNN model inference on CPUs can deliver significant gain to a…