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Found 12 papers, 0 papers with code
Quantization-Guided Training for Compact TinyML Models
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
Quantization for deep neural networks (DNN) have enabled developers to deploy models with less memory and more efficient low-power inference.
Dynamically Throttleable Neural Networks (TNN)
Conditional computation for Deep Neural Networks (DNNs) reduce overall computational load and improve model accuracy by running a subset of the network.
Bit Efficient Quantization for Deep Neural Networks
Quantization for deep neural networks have afforded models for edge devices that use less on-board memory and enable efficient low-power inference.
Generative Memory for Lifelong Reinforcement Learning
Our research is focused on understanding and applying biological memory transfers to new AI systems that can fundamentally improve their performance, throughout their fielded lifetime experience.
Bootstrapping Deep Neural Networks from Approximate Image Processing Pipelines
Complex image processing and computer vision systems often consist of a processing pipeline of functional modules.
Generalized Ternary Connect: End-to-End Learning and Compression of Multiplication-Free Deep Neural Networks
The use of deep neural networks in edge computing devices hinges on the balance between accuracy and complexity of computations.
Power-Grid Controller Anomaly Detection with Enhanced Temporal Deep Learning
Specifically, we first train a temporal deep learning model, using only normal HPC readings from legitimate processes that run daily in these power-grid systems, to model the normal behavior of the power-grid controller.
Bit-Regularized Optimization of Neural Nets
Our key idea is to control the expressive power of the network by dynamically quantizing the range and set of values that the parameters can take.
BitNet: Bit-Regularized Deep Neural Networks
The parameters of neural networks are usually unconstrained and have a dynamic range dispersed over all real values.
GPU Activity Prediction using Representation Learning
GPU activity prediction is an important and complex problem.
Low Precision Neural Networks using Subband Decomposition
In this paper, we present a unique approach using lower precision weights for more efficient and faster training phase.
