Author
Doğan, Eren, Uğurdağ, Hasan Fatih, Ünlü, Hasan
Publication Date
2022
Publication Place
-
IEEE
Type
Document
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
978-166545092-8
Record ID
e84b9b68-0a27-4930-bec3-46f6fc5df6cc
Library Location
Electrical & Electronics Engineering
Date
2022
Sample Text
Applications of artificial neural networks on low-cost embedded systems and microcontrollers (MCUs), has recently been attracting more attention than ever. Since MCUs have limited memory capacity as well as limited compute-speed compared to workstations, employment of current deep learning algorithms on MCUs becomes more practical with the help of model compression. This makes MCUs common and practical alternative solution for autonomous systems. In this paper, we add model compression, specifically Deep Compression, to an existing work, which efficiently deploys PyTorch models on MCUs, in order to increase neural network speed and save electrical power. First, we prune the weight values close to zero in convolutional and fully connected layers. Secondly, the remaining weights and activations are quantized to 8-bit integers from 32-bit floating-point. Finally, forward pass functions are compressed using special data structures for sparse matrices, which store only nonzero weights. In the case of the LeNet-5 model, the memory footprint was reduced by 12.5x, and the inference speed was boosted by 2.6x.
DOI
10.1109/SIU55565.2022.9864848
