Efficient Implementation of Compressed Deep Convolutional Neural Networks

Please enable javascript in your browser.

Afshar, Mohammad | 2018

1614 Viewed

Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 50727 (05)
University: Sharif University of Technology
Department: Electrical Engineering
Advisor(s): Hashemi, Matin
Abstract:
Many mobile applications running on smartphones, wearable devices, tiny autonomous robots and IoT devices would potentially benefit from the accuracy and scalability of deep CNN-based machine learning algorithms. However,performance and energy consumption limitations make the execution of such computationally intensive algorithms on embedded mobile devices prohibitive.We present a GPU-accelerated engine, dubbed mCNN, for execution of trained deep CNNs on mobile platforms. The proposed solution takes the trained model as input and automatically optimizes its parallel implementation on the target mobile platform for efficient use of hardware resources such as mobile GPU threads and SIMD units. Empirical evaluations show that our solution achieves upto 500X speedup
Keywords:
Neural Network ; Convolutional Neural Network ; Increasing Efficiency ; Graphics Procssing Unit (GPU) ; Graphics Procssing Unit (GPU) ; Deep Convolutional Neural Networks