Autori: Arrestad Thea
| Naslov | HGQ: High Granularity Quantization for Real-time Neural Networks on FPGAs (Proceedings Paper) |
| Autori | Sun Chang Que Zhiqiang Arrestad Thea Loncar Vladimir Ngadiuba Jennifer Luk Wayne Spiropulu Maria |
| Info | PROCEEDINGS OF THE 2026 ACM/SIGDA INTERNATIONAL SYMPOSIUM ON FIELD PROGRAMMABLE GATE ARRAYS, FPGA 2026, (2026), vol. br. , str. 79-91 |
| Projekat | United States DoE [DE-SC0011925, DE-FOA-0002705]; NSF [PHY240298, PHY2117997]; United Kingdom EPSRC [UKRI256, EP/V028251/1, EP/N031768/1, EP/S030069/1, EP/X036006/1]; Swiss NSF [PZ00P2_201594]; Schmidt Futures [G-23-64934]; KIAT; Intel; AMD; Caltech Danny Koh graduate student scholarship; ETH/Guenther Dissertori |
| Ispravka | ISI/Web of Science Članak |
