Brandon Reagen
Brandon Reagen
Assistant Professor, New York University
Verified email at - Homepage
Cited by
Cited by
Minerva: Enabling Low-Power, Highly-Accurate Deep Neural Network Accelerators
B Reagen, P Whatmough, R Adolf, S Rama, H Lee, SK Lee, ...
International Symposium on Computer Architecture (ISCA) 43, 267-278, 2016
Machine learning at facebook: Understanding inference at the edge
CJ Wu, D Brooks, K Chen, D Chen, S Choudhury, M Dukhan, ...
2019 IEEE international symposium on high performance computer architecture …, 2019
Aladdin: A pre-rtl, power-performance accelerator simulator enabling large design space exploration of customized architectures
YS Shao, B Reagen, GY Wei, D Brooks
ACM SIGARCH Computer Architecture News 42 (3), 97-108, 2014
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
MachSuite: Benchmarks for Accelerator Design and Customized Architectures
B Reagen, R Adolf, YS Shao, D Wei, Gu-Yeon, Brooks
IISWC, 2014
The architectural implications of facebook's dnn-based personalized recommendation
U Gupta, CJ Wu, X Wang, M Naumov, B Reagen, D Brooks, B Cottel, ...
2020 IEEE International Symposium on High Performance Computer Architecture …, 2020
Recnmp: Accelerating personalized recommendation with near-memory processing
L Ke, U Gupta, BY Cho, D Brooks, V Chandra, U Diril, A Firoozshahian, ...
2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture …, 2020
Fathom: Reference workloads for modern deep learning methods
R Adolf, S Rama, B Reagen, GY Wei, D Brooks
2016 IEEE International Symposium on Workload Characterization (IISWC), 1-10, 2016
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
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
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
Deepreduce: Relu reduction for fast private inference
NK Jha, Z Ghodsi, S Garg, B Reagen
International Conference on Machine Learning, 4839-4849, 2021
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
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
The aladdin approach to accelerator design and modeling
YS Shao, B Reagen, GY Wei, D Brooks
IEEE Micro 35 (3), 58-70, 2015
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
Deep learning for computer architects
B Reagen, R Adolf, P Whatmough, GY Wei, D Brooks, M Martonosi
Morgan & Claypool, 2017
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
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
Porcupine: A synthesizing compiler for vectorized homomorphic encryption
M Cowan, D Dangwal, A Alaghi, C Trippel, VT Lee, B Reagen
Proceedings of the 42nd ACM SIGPLAN International Conference on Programming …, 2021
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