29 citations · 63 across the 7 of their papers we have counts for
7 papers
Automatic Attention Pruning: Improving and Automating Model Pruning using Attentions
Kaiqi Zhao, Animesh Jain, Ming Zhao
Pruning is a promising approach to compress deep learning models in order to deploy them on resource-constrained edge devices. However, many existing pruning solutions are based on…
Indirect noise from weakly reacting inhomogeneities
Animesh Jain, Andrea Giusti, Luca Magri
Indirect noise is a significant contributor to aircraft engine noise, which needs to be minimized in the design of aircraft engines. Indirect noise is caused by the acceleration of…
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…
Adaptive Activation-based Structured Pruning
Kaiqi Zhao, Animesh Jain, Ming Zhao
Pruning is a promising approach to compress complex deep learning models in order to deploy them on resource-constrained edge devices. However, many existing pruning solutions are…
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…