print

DeepHLS: A complete toolchain for automatic synthesis of deep neural networks to FPGA

author
Riazati, Mohammad
Daneshtalab, Masoud
Sjodin, Mikael
Lisper, Bjorn
statement of authorship
Mohammad Riazati, Masoud Daneshtalab, Mikael Sjödin, Björn Lisper
source
ICECS 2020 - 27th IEEE International Conference on Electronics, Circuits and Systems, November 23-25, 2020, Virtual Conference : Proceedings
location of publication
Danvers
publisher
IEEE
year of publication
2020
pages
4 p
conference name, date
2020 27th IEEE International Conference on Electronics, Circuits and Systems (ICECS), November 23-25, 2020
conference location
Glasgow, Scotland
url
https://doi.org/10.1109/ICECS49266.2020.9294881
subject term
tehisnärvivõrgud
VLSI-ahelad
raalprojekteerimine
keyword
deep neural networks
convolutional neural networks (CNNa)
CNN
accelerator architectures
High-Level Synthesis (HLS)
ISBN
978-1-7281-6044-3
notes
Bibliogr.: 28 ref
TTÜ department
arvutisüsteemide instituut
language
inglise
Uurimisrühm
Centre of dependable computing systems
Riazati, M., Daneshtalab, M., Sjodin, M., Lisper, B. DeepHLS: A complete toolchain for automatic synthesis of deep neural networks to FPGA // ICECS 2020 - 27th IEEE International Conference on Electronics, Circuits and Systems, November 23-25, 2020, Virtual Conference : Proceedings. Danvers : IEEE, 2020. 4 p. https://doi.org/10.1109/ICECS49266.2020.9294881