11 citations · 35 across the 11 of their papers we have counts for
11 papers
Quantization-Guided Training for Compact TinyML Models
Sedigh Ghamari, Koray Ozcan, Thu Dinh +4
We propose a Quantization Guided Training (QGT) method to guide DNN training towards optimized low-bit-precision targets and reach extreme compression levels below 8-bit precision.…
Subtensor Quantization for Mobilenets
Thu Dinh, Andrey Melnikov, Vasilios Daskalopoulos +1
Quantization for deep neural networks (DNN) have enabled developers to deploy models with less memory and more efficient low-power inference. However, not all DNN designs are frien…
Dynamically Throttleable Neural Networks (TNN)
Hengyue Liu, Samyak Parajuli, Jesse Hostetler +2
Conditional computation for Deep Neural Networks (DNNs) reduce overall computational load and improve model accuracy by running a subset of the network. In this work, we present a…
Bit Efficient Quantization for Deep Neural Networks
Prateeth Nayak, David Zhang, Sek Chai
Quantization for deep neural networks have afforded models for edge devices that use less on-board memory and enable efficient low-power inference. In this paper, we present a comp…
Generative Memory for Lifelong Reinforcement Learning
Aswin Raghavan, Jesse Hostetler, Sek Chai
Our research is focused on understanding and applying biological memory transfers to new AI systems that can fundamentally improve their performance, throughout their fielded lifet…
Generalized Ternary Connect: End-to-End Learning and Compression of Multiplication-Free Deep Neural Networks
Samyak Parajuli, Aswin Raghavan, Sek Chai
The use of deep neural networks in edge computing devices hinges on the balance between accuracy and complexity of computations. Ternary Connect (TC) \cite{lin2015neural} addresses…