Fractal Synthesis-Reference-Cited by-同舟云学术

Fractal Synthesis

Published:2019-02-20 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2019 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays
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Author:

Langhammer Martin¹,Baeckler Gregg²,Gribok Sergey²

Affiliation:

1. Intel PSG, London, United Kingdom

2. Intel PSG, San Jose, CA, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3289602.3293927

Reference23 articles.

1. 2018. Intel Arria®10 Device Overview. https://www.intel.com/content/dam/ altera-www/global/en_US/pdfs/literature/hb/arria-10/a10_overview.pdf. 2018. Intel Arria®10 Device Overview. https://www.intel.com/content/dam/ altera-www/global/en_US/pdfs/literature/hb/arria-10/a10_overview.pdf.

2. 2018. Intel Stratix®10 GX/SX Device Overview. https://www.intel.com/content/ dam/www/programmable/us/en/pdfs/literature/hb/stratix-10/s10-overview. pdf. 2018. Intel Stratix®10 GX/SX Device Overview. https://www.intel.com/content/ dam/www/programmable/us/en/pdfs/literature/hb/stratix-10/s10-overview. pdf.

3. M. Blott T. Preusser N. Fraser G. Gambardella K. O'Brien and Y. Umuroglu. 2018. FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks. ArXiv e-prints (Sept. 2018). arXiv:1809.04570 M. Blott T. Preusser N. Fraser G. Gambardella K. O'Brien and Y. Umuroglu. 2018. FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks. ArXiv e-prints (Sept. 2018). arXiv:1809.04570

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