29 citations · 53 across the 4 of their papers we have counts for
4 papers
Iterative Activation-based Structured Pruning
Kaiqi Zhao, Animesh Jain, Ming Zhao
Deploying complex deep learning models on edge devices is challenging because they have substantial compute and memory resource requirements, whereas edge devices' resource budget…
UNIT: Unifying Tensorized Instruction Compilation
Jian Weng, Animesh Jain, Jie Wang +3
Because of the increasing demand for computation in DNN, researchers develope both hardware and software mechanisms to reduce the compute and memory burden. A widely adopted approa…
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…
Optimizing Memory-Access Patterns for Deep Learning Accelerators
Hongbin Zheng, Sejong Oh, Huiqing Wang +7
Deep learning (DL) workloads are moving towards accelerators for faster processing and lower cost. Modern DL accelerators are good at handling the large-scale multiply-accumulate o…