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| Title: | HGQ: High Granularity Quantization for Real-time Neural Networks on FPGAs | Authors: | Sun, Chang; Que, Zhiqiang; Arrestad, Thea; Loncar, Vladimir; Ngadiuba, Jennifer; Luk, Wayne; Spiropulu, Maria | Issue Date: | 2026 | Publication: | PROCEEDINGS OF THE 2026 ACM/SIGDA INTERNATIONAL SYMPOSIUM ON FIELD PROGRAMMABLE GATE ARRAYS, FPGA 2026 | Type: | Conference Paper | Collation: | str. 79-91 | DOI: | 10.1145/3748173.3779200 | WoS-ID: | 001722567700017 | URI: | https://enauka.gov.rs/handle/123456789/1037425 | Project: | 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 |
Metadata source: | (Preuzeto iz Nasi u WoS) | M-category: | Mp. category will be shown later |
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