11 citations · 24 across the 7 of their papers we have counts for
6 papers · 1 filter
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.…
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…
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…
GPU Activity Prediction using Representation Learning
Aswin Raghavan, Mohamed Amer, Timothy Shields +2
GPU activity prediction is an important and complex problem. This is due to the high level of contention among thousands of parallel threads. This problem was mostly addressed usin…
Low Precision Neural Networks using Subband Decomposition
Sek Chai, Aswin Raghavan, David Zhang +2
Large-scale deep neural networks (DNN) have been successfully used in a number of tasks from image recognition to natural language processing. They are trained using large training…