Energy-Efficient CNN Personalized Training by Adaptive Data Reformation

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To adopt deep neural networks in resource constrained edge devices, various energy-and memory-efficient embedded accelerators have been proposed. However, most off-the-shelf networks are well-trained with vast amounts of data, but unexplored users’ data or accelerator’s constraints can lead to unexpected accuracy loss. Therefore, a network adaptation suitable for each user and device is essential to make a high confidence prediction in given environment. We propose simple but efficient data reformation methods that can effectively reduce the communication cost with off-chip memory during the adaptation. Our proposal utilizes the data’s zero-centered distribution and spatial correlation to concentrate the sporadically spread bit-level zeros to the units of value. Consequently, we reduced communication volume by up to 55.6% per task with an area overhead of 0.79% during the personalization training.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2023-01
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, v.42, no.1, pp.332 - 336

ISSN
0278-0070
DOI
10.1109/TCAD.2022.3170845
URI
http://hdl.handle.net/10203/303928
Appears in Collection
EE-Journal Papers(저널논문)
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