16 citations · 69 across the 23 of their papers we have counts for
7 papers · 1 filter
Demystifying Map Space Exploration for NPUs
Sheng-Chun Kao, Angshuman Parashar, Po-An Tsai +1
Map Space Exploration is the problem of finding optimized mappings of a Deep Neural Network (DNN) model on an accelerator. It is known to be extremely computationally expensive, an…
Training Recipe for N:M Structured Sparsity with Decaying Pruning Mask
Sheng-Chun Kao, Amir Yazdanbakhsh, Suvinay Subramanian +3
Sparsity has become one of the promising methods to compress and accelerate Deep Neural Networks (DNNs). Among different categories of sparsity, structured sparsity has gained more…
AIRCHITECT: Learning Custom Architecture Design and Mapping Space
Ananda Samajdar, Jan Moritz Joseph, Matthew Denton +1
Design space exploration is an important but costly step involved in the design/deployment of custom architectures to squeeze out maximum possible performance and energy efficiency…
Restructuring, Pruning, and Adjustment of Deep Models for Parallel Distributed Inference
Afshin Abdi, Saeed Rashidi, Faramarz Fekri +1
Using multiple nodes and parallel computing algorithms has become a principal tool to improve training and execution times of deep neural networks as well as effective collective i…
Generative Design of Hardware-aware DNNs
Sheng-Chun Kao, Arun Ramamurthy, Tushar Krishna
To efficiently run DNNs on the edge/cloud, many new DNN inference accelerators are being designed and deployed frequently. To enhance the resource efficiency of DNNs, model quantiz…
Conditional Neural Architecture Search
Sheng-Chun Kao, Arun Ramamurthy, Reed Williams +1
Designing resource-efficient Deep Neural Networks (DNNs) is critical to deploy deep learning solutions over edge platforms due to diverse performance, power, and memory budgets. Un…