Machine Learning Enhanced Model Selection Methodologies and Optimization of Deep Learning (DL) Inference for accuracy, inference time and energy on Mobile and Embedded Systems

Project: Monitored by Research Administration

Project Details

Grant Program

Faculty Development Competitive Research Grant Program 2021-2023

Project Description

In this project, the investigators will characterize mobile gaming and Deep Learning (esp. Convolutional Neural Networks) workloads (i.e., CPU or GPU dominant workloads in gaming; and loading and inference—convolution, pooling and fully connection layers-- workloads in DL) using collected data such as utilization, frequency, performance metrics and power consumption on the mobile platforms. As a further phase, we build models from a simple yet effective heuristic model based on the characterization to more sophisticated ML enhanced models considering both accuracy and interpretability; and then, we propose Dynamic Power Management (DPM) strategies that orchestrate energy-efficient policies among heterogeneous processors and evaluate the strategies using gaming and DL test datasets. As the last phase, we introduce energy-efficient DL inference optimization through adaptive model selection for mobile and embedded systems based on all the previous observations and analyses.
StatusNot started

Keywords

  • Machine Learning Model Selection
  • Mobile Deep Learning (DL)
  • Convolutional Neural Networks (CNNs)
  • Integrated GPU
  • Dynamic Power Management (DPM) Policies
  • Embedded Systems

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