Harnessing Numerical Flexibility for Deep Learning on FPGAs
Andrew C. Ling, Mohamed S. Abdelfattah, Shane O’Connell, Andrew Bitar, David K. Han, Roberto DiCecco, Suchit Subhaschandra, Chris N. Johnson, Dmitry Denisenko, Josh Fender, Gordon R. Chiu · 2018
Deep learning has become a key workload in the data centre and edge leading to an arms race for compute dominance in this space. FPGAs have shown they can compete by combining deterministic low-latency with high throughput and flexibility. In particular, due to FPGAs' bit-level programmability, FPGAs can efficiently implement arbitrary precisions and numeric data types which is critical to fast evolving fields like deep learning.