Zobrazeno 1 - 10
of 67
pro vyhledávání: '"Peter Milder"'
Publikováno v:
Transactions on Cryptographic Hardware and Embedded Systems, Vol 2018, Iss 3 (2018)
This paper presents a hardware implementation of a Residue Polynomial Multiplier (RPM), designed to accelerate the full Residue Number System (RNS) variant of the Fan-Vercauteren scheme proposed by Bajard et al. [BEHZ16]. Our design speeds up polynom
Externí odkaz:
https://doaj.org/article/339be0f47f0c486bb6b532aadce24204
Publikováno v:
Proceedings of the Great Lakes Symposium on VLSI 2023.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::78be03b70930277cc4c8872ca985552b
https://doi.org/10.1145/3583781.3590202
https://doi.org/10.1145/3583781.3590202
Publikováno v:
Proceedings of the 9th ACM Conference on Information-Centric Networking.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ff6139e6109ae794c5b2263a123379ca
https://doi.org/10.1145/3517212.3558090
https://doi.org/10.1145/3517212.3558090
Publikováno v:
ACM Transactions on Reconfigurable Technology and Systems. 14:1-18
Software verification is an important stage of the software development process, particularly for mission-critical systems. As the traditional methodology of using unit tests falls short of verifying complex software, developers are increasingly rely
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::40929c709ea401a1a8cd4d6a4ec051a7
https://doi.org/10.1145/3448272
https://doi.org/10.1145/3448272
Publikováno v:
IEEE Transactions on Computers. 71:3072-3073
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ff4b17a97d5b4329aeba99039a6eb414
https://doi.org/10.1109/tc.2022.3216428
https://doi.org/10.1109/tc.2022.3216428
Publikováno v:
IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 28:1665-1675
Energy efficiency is a critical design objective in deep learning hardware, particularly for real-time machine learning applications where the processing takes place on resource-constrained platforms. The inherent resilience of these applications to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c1e8e787d63ea7a88bc380195d76ab6a
https://doi.org/10.1109/tvlsi.2020.2988204
https://doi.org/10.1109/tvlsi.2020.2988204
Publikováno v:
MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0337c78f5612a46c810b21f0b7031fb3
https://doi.org/10.1109/milcom52596.2021.9652896
https://doi.org/10.1109/milcom52596.2021.9652896
Publikováno v:
ICC
With the plethora of wireless devices in limited spaces running multiple WiFi standards (802.11 a/b/g/n/ac), gaining an understanding of network latency, loss, and medium utilization becomes extremely challenging. Microsecond timing fidelity with pro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e00286cdc0f4bffc9c312bc06e33ac92
https://doi.org/10.1109/icc42927.2021.9500688
https://doi.org/10.1109/icc42927.2021.9500688
Publikováno v:
IEEE Micro. 39:17-25
In this article, we present Argus, an end-to-end framework for accelerating convolutional neural networks (CNNs) on field-programmable gate arrays (FPGAs) with minimum user effort. Argus uses state-of-the-art methods to auto-generate highly optimized
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7cd6ca3a3dc82e175b4a928d0a5fe117
https://doi.org/10.1109/mm.2019.2930607
https://doi.org/10.1109/mm.2019.2930607
Autor:
Tianchu Ji, Shraddhan Jain, H. Andrew Schwartz, Michael Ferdman, Peter Milder, Niranjan Balasubramanian
Publikováno v:
ACL/IJCNLP (Findings)
How much information do NLP tasks really need from a transformer's attention mechanism at application-time (inference)? From recent work, we know that there is sparsity in transformers and that the floating-points within its computation can be discre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eb117c10712375766bd293472d9365d3
https://doi.org/10.18653/v1/2021.findings-acl.363
https://doi.org/10.18653/v1/2021.findings-acl.363