@inproceedings{2351aead1e1f4c409ee400da8174c4b6,
title = "TrainWare: A memory optimized weight update architecture for on-device convolutional neural network training",
abstract = "Training convolutional neural network on device has become essential where it allows applications to consider user's individual environment. Meanwhile, the weight update operation from the training process is the primary factor of high energy consumption due to its substantial memory accesses. We propose a dedicated weight update architecture with two key features: (1) a specialized local buffer for the DRAM access deduction (2) a novel dataflow and its suitable processing element array structure for weight gradient computation to optimize the energy consumed by internal memories. Our scheme achieves 14.3%-30.2% total energy reduction by drastically eliminating the memory accesses.",
keywords = "CNN, Dataflow, On-device, Training architecture, Weight update",
author = "Seungkyu Choi and Jaehyeong Sim and Myeonggu Kang and Kim, {Lee Sup}",
note = "Publisher Copyright: {\textcopyright} 2018 Association for Computing Machinery.; 23rd IEEE/ACM International Symposium on Low Power Electronics and Design, ISLPED 2018 ; Conference date: 23-07-2018 Through 25-07-2018",
year = "2018",
month = jul,
day = "23",
doi = "10.1145/3218603.3218625",
language = "English",
isbn = "9781450357043",
series = "Proceedings of the International Symposium on Low Power Electronics and Design",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ISLPED 2018 - Proceedings of the 2018 International Symposium on Low Power Electronics and Design",
}
