Общедоступные статьи - Brandon ReagenПодробнее...
3 статьи недоступны нигде
Exploring the Efficiency of Data-Oblivious Programs
L Biernacki, BM Tiruye, MZ Demissie, FA Andargie, B Reagen, T Austin
2023 IEEE International Symposium on Performance Analysis of Systems and …, 2023
Финансирование: US Department of Defense
Quantifying the Overheads of Modular Multiplication
D Soni, M Nabeel, N Neda, R Karri, M Maniatakos, B Reagen
2023 IEEE/ACM International Symposium on Low Power Electronics and Design …, 2023
Финансирование: US National Science Foundation, US Department of Defense
Methods and infrastructure in the era of accelerator-centric architectures
B Reagen, YS Shao, SL Xi, GY Wei, D Brooks
2017 IEEE 60th International Midwest Symposium on Circuits and Systems …, 2017
Финансирование: US Department of Defense
25 статей доступны в некоторых источниках
Ares: A framework for quantifying the resilience of deep neural networks
B Reagen, U Gupta, L Pentecost, P Whatmough, SK Lee, N Mulholland, ...
Proceedings of the 55th Annual Design Automation Conference, 1-6, 2018
Финансирование: US National Science Foundation, US Department of Defense
Deeprecsys: A system for optimizing end-to-end at-scale neural recommendation inference
U Gupta, S Hsia, V Saraph, X Wang, B Reagen, GY Wei, HHS Lee, ...
2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture …, 2020
Финансирование: US National Science Foundation
Cheetah: Optimizing and accelerating homomorphic encryption for private inference
B Reagen, WS Choi, Y Ko, VT Lee, HHS Lee, GY Wei, D Brooks
2021 IEEE International Symposium on High-Performance Computer Architecture …, 2021
Финансирование: US Department of Defense
A case for efficient accelerator design space exploration via bayesian optimization
B Reagen, JM Hernández-Lobato, R Adolf, M Gelbart, P Whatmough, ...
2017 IEEE/ACM International Symposium on Low Power Electronics and Design …, 2017
Финансирование: US Department of Defense
Deepreduce: Relu reduction for fast private inference
NK Jha, Z Ghodsi, S Garg, B Reagen
International Conference on Machine Learning, 4839-4849, 2021
Финансирование: US Department of Defense
Cryptonas: Private inference on a relu budget
Z Ghodsi, AK Veldanda, B Reagen, S Garg
Advances in Neural Information Processing Systems 33, 16961-16971, 2020
Финансирование: US National Science Foundation
Masr: A modular accelerator for sparse rnns
U Gupta, B Reagen, L Pentecost, M Donato, T Tambe, AM Rush, GY Wei, ...
2019 28th International Conference on Parallel Architectures and Compilation …, 2019
Финансирование: US National Science Foundation, US Department of Defense
Weightless: Lossy weight encoding for deep neural network compression
B Reagan, U Gupta, B Adolf, M Mitzenmacher, A Rush, GY Wei, D Brooks
International Conference on Machine Learning, 4324-4333, 2018
Финансирование: US National Science Foundation, US Department of Defense
Selective network linearization for efficient private inference
M Cho, A Joshi, B Reagen, S Garg, C Hegde
International Conference on Machine Learning, 3947-3961, 2022
Финансирование: US National Science Foundation, US Department of Defense, US Department of …
Maxnvm: Maximizing dnn storage density and inference efficiency with sparse encoding and error mitigation
L Pentecost, M Donato, B Reagen, U Gupta, S Ma, GY Wei, D Brooks
Proceedings of the 52Nd Annual IEEE/ACM International Symposium on …, 2019
Финансирование: US Department of Defense
Circa: Stochastic relus for private deep learning
Z Ghodsi, NK Jha, B Reagen, S Garg
Advances in Neural Information Processing Systems 34, 2241-2252, 2021
Финансирование: US National Science Foundation, US Department of Defense
On-chip deep neural network storage with multi-level eNVM
M Donato, B Reagen, L Pentecost, U Gupta, D Brooks, GY Wei
Proceedings of the 55th Annual Design Automation Conference, 1-6, 2018
Финансирование: US National Science Foundation, US Department of Defense
A fully integrated battery-powered system-on-chip in 40-nm CMOS for closed-loop control of insect-scale pico-aerial vehicle
X Zhang, M Lok, T Tong, SK Lee, B Reagen, S Chaput, PEJ Duhamel, ...
IEEE Journal of Solid-State Circuits 52 (9), 2374-2387, 2017
Финансирование: US National Science Foundation, US Department of Defense
Characterizing and optimizing end-to-end systems for private inference
K Garimella, Z Ghodsi, NK Jha, S Garg, B Reagen
Proceedings of the 28th ACM International Conference on Architectural …, 2023
Финансирование: US National Science Foundation, US Department of Defense
Designing neural network hardware accelerators with decoupled objective evaluations
JM Hernández-Lobato, MA Gelbart, B Reagen, R Adolf, ...
NIPS workshop on Bayesian Optimization 10, 2016
Финансирование: Government of Spain
Haac: A hardware-software co-design to accelerate garbled circuits
J Mo, J Gopinath, B Reagen
Proceedings of the 50th Annual International Symposium on Computer …, 2023
Финансирование: US Department of Defense
RPU: The Ring Processing Unit
D Soni, N Neda, N Zhang, B Reynwar, H Gamil, B Heyman, M Nabeel, ...
arXiv preprint arXiv:2303.17118, 2023
Финансирование: US Department of Defense
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