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2020 – today
- 2024
[j6]Nandan Kumar Jha, Brandon Reagen:
DeepReShape: Redesigning Neural Networks for Efficient Private Inference. Trans. Mach. Learn. Res. 2024 (2024)- [c39]Alhad Daftardar
, Brandon Reagen, Siddharth Garg:
SZKP: A Scalable Accelerator Architecture for Zero-Knowledge Proofs. PACT 2024: 271-283 - [c38]Negar Neda, Austin Ebel, Benedict Reynwar, Brandon Reagen:
CiFlow: Dataflow Analysis and Optimization of Key Switching for Homomorphic Encryption. ISPASS 2024: 61-72 - [i37]Juran Ding, Yuanzhe Liu, Lingbin Sun, Brandon Reagen:
NTTSuite: Number Theoretic Transform Benchmarks for Accelerating Encrypted Computation. CoRR abs/2405.11353 (2024) - [i36]Alhad Daftardar, Brandon Reagen, Siddharth Garg:
SZKP: A Scalable Accelerator Architecture for Zero-Knowledge Proofs. CoRR abs/2408.05890 (2024) - [i35]Austin Ebel, Brandon Reagen:
Osiris: A Systolic Approach to Accelerating Fully Homomorphic Encryption. CoRR abs/2408.09593 (2024) - [i34]Nandan Kumar Jha, Brandon Reagen:
ReLU's Revival: On the Entropic Overload in Normalization-Free Large Language Models. CoRR abs/2410.09637 (2024) - [i33]Nandan Kumar Jha, Brandon Reagen:
AERO: Softmax-Only LLMs for Efficient Private Inference. CoRR abs/2410.13060 (2024) - [i32]Patrick Yubeaton, Jianqiao Mo, Karthik Garimella, Nandan Kumar Jha, Brandon Reagen, Chinmay Hegde, Siddharth Garg:
TruncFormer: Private LLM Inference Using Only Truncations. CoRR abs/2412.01042 (2024) - 2023
- [c37]Karthik Garimella, Zahra Ghodsi, Nandan Kumar Jha, Siddharth Garg, Brandon Reagen:
Characterizing and Optimizing End-to-End Systems for Private Inference. ASPLOS (3) 2023: 89-104 - [c36]Jianqiao Mo, Karthik Garimella, Negar Neda, Austin Ebel, Brandon Reagen:
Towards Fast and Scalable Private Inference. CF 2023: 322-328 - [c35]Naifeng Zhang, Austin Ebel, Negar Neda, Patrick Brinich, Benedict Reynwar, Andrew G. Schmidt, Mike Franusich, Jeremy Johnson, Brandon Reagen, Franz Franchetti:
Generating High-Performance Number Theoretic Transform Implementations for Vector Architectures. HPEC 2023: 1-7 - [c34]Jianqiao Mo, Jayanth Gopinath, Brandon Reagen:
HAAC: A Hardware-Software Co-Design to Accelerate Garbled Circuits. ISCA 2023: 10:1-10:13 - [c33]Deepraj Soni, Mohammed Nabeel, Negar Neda, Ramesh Karri, Michail Maniatakos, Brandon Reagen:
Quantifying the Overheads of Modular Multiplication. ISLPED 2023: 1-6 - [c32]Lauren Biernacki, Biniyam Mengist Tiruye, Meron Zerihun Demissie, Fitsum Assamnew Andargie, Brandon Reagen, Todd M. Austin:
Exploring the Efficiency of Data-Oblivious Programs. ISPASS 2023: 189-200 - [c31]Deepraj Soni, Negar Neda, Naifeng Zhang, Benedict Reynwar, Homer Gamil, Benjamin Heyman, Mohammed Nabeel, Ahmad Al Badawi, Yuriy Polyakov, Kellie Canida, Massoud Pedram, Michail Maniatakos, David Bruce Cousins, Franz Franchetti, Matthew French, Andrew G. Schmidt, Brandon Reagen:
RPU: The Ring Processing Unit. ISPASS 2023: 272-282 - [c30]Deepraj Soni, Mohammed Nabeel, Homer Gamil, Oleg Mazonka, Brandon Reagen, Ramesh Karri, Michail Maniatakos:
Design Space Exploration of Modular Multipliers for ASIC FHE accelerators. ISQED 2023: 1-8 - [i31]Deepraj Soni, Negar Neda, Naifeng Zhang, Benedict Reynwar, Homer Gamil, Benjamin Heyman, Mohammed Thari Nabeel, Ahmad Al Badawi, Yuriy Polyakov, Kellie Canida, Massoud Pedram, Michail Maniatakos, David Bruce Cousins, Franz Franchetti, Matthew French, Andrew G. Schmidt, Brandon Reagen:
RPU: The Ring Processing Unit. CoRR abs/2303.17118 (2023) - [i30]David Bruce Cousins, Yuriy Polyakov, Ahmad Al Badawi, Matthew French, Andrew G. Schmidt, Ajey P. Jacob, Benedict Reynwar, Kellie Canida, Akhilesh R. Jaiswal, Clynn Mathew, Homer Gamil, Negar Neda, Deepraj Soni, Michail Maniatakos, Brandon Reagen, Naifeng Zhang, Franz Franchetti, Patrick Brinich, Jeremy Johnson, Patrick Broderick, Mike Franusich, Bo Zhang, Zeming Cheng, Massoud Pedram:
TREBUCHET: Fully Homomorphic Encryption Accelerator for Deep Computation. CoRR abs/2304.05237 (2023) - [i29]Nandan Kumar Jha, Brandon Reagen:
DeepReShape: Redesigning Neural Networks for Efficient Private Inference. CoRR abs/2304.10593 (2023) - [i28]Jianqiao Mo, Karthik Garimella, Negar Neda, Austin Ebel, Brandon Reagen:
Towards Fast and Scalable Private Inference. CoRR abs/2307.04077 (2023) - [i27]Haoran Geng, Jianqiao Mo, Dayane Reis, Jonathan Takeshita, Taeho Jung, Brandon Reagen, Michael T. Niemier, Xiaobo Sharon Hu:
Privacy Preserving In-memory Computing Engine. CoRR abs/2308.02648 (2023) - [i26]Naren Dhyani, Jianqiao Mo, Minsu Cho, Ameya Joshi, Siddharth Garg, Brandon Reagen, Chinmay Hegde:
PriViT: Vision Transformers for Fast Private Inference. CoRR abs/2310.04604 (2023) - [i25]Negar Neda, Austin Ebel, Benedict Reynwar, Brandon Reagen:
CiFlow: Dataflow Analysis and Optimization of Key Switching for Homomorphic Encryption. CoRR abs/2311.01598 (2023) - [i24]Austin Ebel, Karthik Garimella, Brandon Reagen:
Orion: A Fully Homomorphic Encryption Compiler for Private Deep Neural Network Inference. CoRR abs/2311.03470 (2023) - [i23]Deepraj Soni, Negar Neda, Naifeng Zhang, Benedict Reynwar, Homer Gamil, Benjamin Heyman, Mohammed Thari Nabeel, Ahmad Al Badawi, Yuriy Polyakov, Kellie Canida, Massoud Pedram, Michail Maniatakos, David Bruce Cousins, Franz Franchetti, Matthew French, Andrew G. Schmidt, Brandon Reagen:
RPU: The Ring Processing Unit. IACR Cryptol. ePrint Arch. 2023: 465 (2023) - [i22]David Bruce Cousins, Yuriy Polyakov, Ahmad Al Badawi, Matthew French, Andrew G. Schmidt, Ajey P. Jacob, Benedict Reynwar, Kellie Canida, Akhilesh R. Jaiswal, Clynn Mathew, Homer Gamil, Negar Neda, Deepraj Soni, Michail Maniatakos, Brandon Reagen, Naifeng Zhang, Franz Franchetti, Patrick Brinich, Jeremy Johnson, Patrick Broderick, Mike Franusich, Bo Zhang, Zeming Cheng, Massoud Pedram:
TREBUCHET: Fully Homomorphic Encryption Accelerator for Deep Computation. IACR Cryptol. ePrint Arch. 2023: 521 (2023) - 2022
- [j5]Minsu Cho, Zahra Ghodsi, Brandon Reagen, Siddharth Garg, Chinmay Hegde:
Sphynx: A Deep Neural Network Design for Private Inference. IEEE Secur. Priv. 20(5): 22-34 (2022) - [c29]Minsu Cho, Ameya Joshi, Brandon Reagen, Siddharth Garg, Chinmay Hegde:
Selective Network Linearization for Efficient Private Inference. ICML 2022: 3947-3961 - [i21]Minsu Cho, Ameya Joshi, Siddharth Garg, Brandon Reagen, Chinmay Hegde:
Selective Network Linearization for Efficient Private Inference. CoRR abs/2202.02340 (2022) - [i20]Hsuan Hsiao, Vincent T. Lee, Brandon Reagen, Armin Alaghi:
Homomorphically Encrypted Computation using Stochastic Encodings. CoRR abs/2203.02547 (2022) - [i19]Shaowei Zhu, Hyo Jin Kim, Maurizio Monge, G. Edward Suh, Armin Alaghi, Brandon Reagen, Vincent T. Lee:
Verifiable Access Control for Augmented Reality Localization and Mapping. CoRR abs/2203.13308 (2022) - [i18]Wooseok Choi, Brandon Reagen, Gu-Yeon Wei, David Brooks:
Impala: Low-Latency, Communication-Efficient Private Deep Learning Inference. CoRR abs/2205.06437 (2022) - [i17]Karthik Garimella, Zahra Ghodsi, Nandan Kumar Jha, Siddharth Garg, Brandon Reagen:
Characterizing and Optimizing End-to-End Systems for Private Inference. CoRR abs/2207.07177 (2022) - [i16]Jianqiao Mo, Jayanth Gopinath, Brandon Reagen:
HAAC: A Hardware-Software Co-Design to Accelerate Garbled Circuits. CoRR abs/2211.13324 (2022) - 2021
- [c28]Deeksha Dangwal, Vincent T. Lee, Hyo Jin Kim, Tianwei Shen, Meghan Cowan, Rajvi Shah, Caroline Trippel, Brandon Reagen, Timothy Sherwood, Vasileios Balntas, Armin Alaghi, Eddy Ilg:
Mitigating Reverse Engineering Attacks on Local Feature Descriptors. BMVC 2021: 106 - [c27]Brandon Reagen, Wooseok Choi, Yeongil Ko, Vincent T. Lee, Hsien-Hsin S. Lee, Gu-Yeon Wei, David Brooks:
Cheetah: Optimizing and Accelerating Homomorphic Encryption for Private Inference. HPCA 2021: 26-39 - [c26]Nandan Kumar Jha, Zahra Ghodsi, Siddharth Garg, Brandon Reagen:
DeepReDuce: ReLU Reduction for Fast Private Inference. ICML 2021: 4839-4849 - [c25]Zahra Ghodsi, Nandan Kumar Jha, Brandon Reagen, Siddharth Garg:
Circa: Stochastic ReLUs for Private Deep Learning. NeurIPS 2021: 2241-2252 - [c24]Meghan Cowan, Deeksha Dangwal, Armin Alaghi, Caroline Trippel, Vincent T. Lee, Brandon Reagen:
Porcupine: a synthesizing compiler for vectorized homomorphic encryption. PLDI 2021: 375-389 - [c23]Lauren Biernacki, Meron Zerihun Demissie, Kidus Birkayehu Workneh, Galane Basha Namomsa, Plato Gebremedhin, Fitsum Assamnew Andargie, Brandon Reagen, Todd M. Austin:
VIP-Bench: A Benchmark Suite for Evaluating Privacy-Enhanced Computation Frameworks. SEED 2021: 139-149 - [i15]Meghan Cowan, Deeksha Dangwal, Armin Alaghi, Caroline Trippel, Vincent T. Lee, Brandon Reagen:
Porcupine: A Synthesizing Compiler for Vectorized Homomorphic Encryption. CoRR abs/2101.07841 (2021) - [i14]Nandan Kumar Jha, Zahra Ghodsi, Siddharth Garg, Brandon Reagen:
DeepReDuce: ReLU Reduction for Fast Private Inference. CoRR abs/2103.01396 (2021) - [i13]Deeksha Dangwal, Meghan Cowan, Armin Alaghi, Vincent T. Lee, Brandon Reagen, Caroline Trippel:
SoK: Opportunities for Software-Hardware-Security Codesign for Next Generation Secure Computing. CoRR abs/2105.00378 (2021) - [i12]Deeksha Dangwal, Vincent T. Lee, Hyo Jin Kim, Tianwei Shen, Meghan Cowan, Rajvi Shah, Caroline Trippel, Brandon Reagen, Timothy Sherwood, Vasileios Balntas, Armin Alaghi, Eddy Ilg:
Analysis and Mitigations of Reverse Engineering Attacks on Local Feature Descriptors. CoRR abs/2105.03812 (2021) - [i11]Zahra Ghodsi, Nandan Kumar Jha, Brandon Reagen, Siddharth Garg:
Circa: Stochastic ReLUs for Private Deep Learning. CoRR abs/2106.08475 (2021) - [i10]Minsu Cho, Zahra Ghodsi, Brandon Reagen, Siddharth Garg, Chinmay Hegde:
Sphynx: ReLU-Efficient Network Design for Private Inference. CoRR abs/2106.11755 (2021) - [i9]Karthik Garimella, Nandan Kumar Jha, Brandon Reagen:
Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning. CoRR abs/2107.12342 (2021) - [i8]Karthik Garimella, Nandan Kumar Jha, Zahra Ghodsi, Siddharth Garg, Brandon Reagen:
CryptoNite: Revealing the Pitfalls of End-to-End Private Inference at Scale. CoRR abs/2111.02583 (2021) - 2020
- [c22]Udit Gupta, Carole-Jean Wu, Xiaodong Wang, Maxim Naumov, Brandon Reagen, David Brooks, Bradford Cottel, Kim M. Hazelwood, Mark Hempstead, Bill Jia, Hsien-Hsin S. Lee, Andrey Malevich, Dheevatsa Mudigere, Mikhail Smelyanskiy, Liang Xiong, Xuan Zhang:
The Architectural Implications of Facebook's DNN-Based Personalized Recommendation. HPCA 2020: 488-501 - [c21]Liu Ke, Udit Gupta, Benjamin Youngjae Cho, David Brooks, Vikas Chandra, Utku Diril, Amin Firoozshahian, Kim M. Hazelwood, Bill Jia, Hsien-Hsin S. Lee, Meng Li, Bert Maher, Dheevatsa Mudigere, Maxim Naumov, Martin Schatz, Mikhail Smelyanskiy, Xiaodong Wang, Brandon Reagen, Carole-Jean Wu, Mark Hempstead, Xuan Zhang:
RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing. ISCA 2020: 790-803 - [c20]Udit Gupta, Samuel Hsia, Vikram Saraph, Xiaodong Wang, Brandon Reagen, Gu-Yeon Wei, Hsien-Hsin S. Lee, David Brooks, Carole-Jean Wu:
DeepRecSys: A System for Optimizing End-To-End At-Scale Neural Recommendation Inference. ISCA 2020: 982-995 - [c19]Deeksha Dangwal, Meghan Cowan, Armin Alaghi, Vincent T. Lee, Brandon Reagen, Caroline Trippel:
SoK: Opportunities for Software-Hardware-Security Codesign for Next Generation Secure Computing. HASP@MICRO 2020: 8:1-8:9 - [c18]Zahra Ghodsi, Akshaj Kumar Veldanda, Brandon Reagen, Siddharth Garg:
CryptoNAS: Private Inference on a ReLU Budget. NeurIPS 2020 - [i7]Udit Gupta, Samuel Hsia, Vikram Saraph, Xiaodong Wang, Brandon Reagen, Gu-Yeon Wei, Hsien-Hsin S. Lee, David Brooks, Carole-Jean Wu:
DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference. CoRR abs/2001.02772 (2020) - [i6]Brandon Reagen, Wooseok Choi, Yeongil Ko, Vincent T. Lee, Gu-Yeon Wei, Hsien-Hsin S. Lee, David Brooks:
Cheetah: Optimizations and Methods for PrivacyPreserving Inference via Homomorphic Encryption. CoRR abs/2006.00505 (2020) - [i5]Zahra Ghodsi, Akshaj Kumar Veldanda, Brandon Reagen, Siddharth Garg:
CryptoNAS: Private Inference on a ReLU Budget. CoRR abs/2006.08733 (2020)
2010 – 2019
- 2019
- [c17]Udit Gupta, Brandon Reagen, Lillian Pentecost, Marco Donato, Thierry Tambe, Alexander M. Rush, Gu-Yeon Wei, David Brooks:
MASR: A Modular Accelerator for Sparse RNNs. PACT 2019: 1-14 - [c16]Carole-Jean Wu, David Brooks, Kevin Chen, Douglas Chen, Sy Choudhury, Marat Dukhan, Kim M. Hazelwood, Eldad Isaac, Yangqing Jia, Bill Jia, Tommer Leyvand, Hao Lu, Yang Lu, Lin Qiao, Brandon Reagen, Joe Spisak, Fei Sun, Andrew Tulloch, Peter Vajda, Xiaodong Wang, Yanghan Wang, Bram Wasti, Yiming Wu, Ran Xian, Sungjoo Yoo, Peizhao Zhang:
Machine Learning at Facebook: Understanding Inference at the Edge. HPCA 2019: 331-344 - [c15]Yu Emma Wang, Yuhao Zhu, Glenn G. Ko, Brandon Reagen, Gu-Yeon Wei, David Brooks:
Demystifying Bayesian Inference Workloads. ISPASS 2019: 177-189 - [c14]Lillian Pentecost, Marco Donato, Brandon Reagen, Udit Gupta, Siming Ma, Gu-Yeon Wei, David Brooks:
MaxNVM: Maximizing DNN Storage Density and Inference Efficiency with Sparse Encoding and Error Mitigation. MICRO 2019: 769-781 - [i4]Udit Gupta, Xiaodong Wang, Maxim Naumov, Carole-Jean Wu, Brandon Reagen, David Brooks, Bradford Cottel, Kim M. Hazelwood, Bill Jia, Hsien-Hsin S. Lee, Andrey Malevich, Dheevatsa Mudigere, Mikhail Smelyanskiy, Liang Xiong, Xuan Zhang:
The Architectural Implications of Facebook's DNN-based Personalized Recommendation. CoRR abs/1906.03109 (2019) - [i3]Liu Ke, Udit Gupta, Carole-Jean Wu, Benjamin Youngjae Cho, Mark Hempstead, Brandon Reagen, Xuan Zhang, David M. Brooks, Vikas Chandra, Utku Diril, Amin Firoozshahian, Kim M. Hazelwood, Bill Jia, Hsien-Hsin S. Lee, Meng Li, Bert Maher, Dheevatsa Mudigere, Maxim Naumov, Martin Schatz, Mikhail Smelyanskiy, Xiaodong Wang:
RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing. CoRR abs/1912.12953 (2019) - 2018
- [j4]Rafael Garibotti, Brandon Reagen, Yakun Sophia Shao, Gu-Yeon Wei, David M. Brooks:
Assisting High-Level Synthesis Improve SpMV Benchmark Through Dynamic Dependence Analysis. IEEE Trans. Circuits Syst. II Express Briefs 65-II(10): 1440-1444 (2018) - [c13]Brandon Reagen, Udit Gupta, Lillian Pentecost, Paul N. Whatmough, Sae Kyu Lee, Niamh Mulholland, David M. Brooks, Gu-Yeon Wei:
Ares: a framework for quantifying the resilience of deep neural networks. DAC 2018: 17:1-17:6 - [c12]Marco Donato, Brandon Reagen, Lillian Pentecost, Udit Gupta, David Brooks, Gu-Yeon Wei:
On-chip deep neural network storage with multi-level eNVM. DAC 2018: 169:1-169:6 - [c11]Brandon Reagen, Udit Gupta, Robert Adolf, Michael Mitzenmacher, Alexander M. Rush, Gu-Yeon Wei, David Brooks:
Weightless: Lossy weight encoding for deep neural network compression. ICLR (Workshop) 2018 - [c10]Brandon Reagen, Udit Gupta, Bob Adolf, Michael Mitzenmacher, Alexander M. Rush, Gu-Yeon Wei, David Brooks:
Weightless: Lossy weight encoding for deep neural network compression. ICML 2018: 4321-4330 - 2017
- [b1]Brandon Reagen, Robert Adolf, Paul N. Whatmough, Gu-Yeon Wei, David M. Brooks:
Deep Learning for Computer Architects. Synthesis Lectures on Computer Architecture, Morgan & Claypool Publishers 2017, ISBN 978-3-031-00628-9 - [j3]Xuan Zhang, Mario Lok, Tao Tong, Sae Kyu Lee, Brandon Reagen, Simon Chaput, Pierre-Emile J. Duhamel, Robert J. Wood, David M. Brooks, Gu-Yeon Wei:
A Fully Integrated Battery-Powered System-on-Chip in 40-nm CMOS for Closed-Loop Control of Insect-Scale Pico-Aerial Vehicle. IEEE J. Solid State Circuits 52(9): 2374-2387 (2017) - [j2]Yuhao Zhu, Vijay Janapa Reddi, Robert Adolf, Saketh Rama, Brandon Reagen, Gu-Yeon Wei, David M. Brooks:
Cognitive Computing Safety: The New Horizon for Reliability / The Design and Evolution of Deep Learning Workloads. IEEE Micro 37(1): 15-21 (2017) - [c9]Rafael Garibotti, Brandon Reagen, Yakun Sophia Shao, Gu-Yeon Wei, David M. Brooks:
Using dynamic dependence analysis to improve the quality of high-level synthesis designs. ISCAS 2017: 1-4 - [c8]Brandon Reagen, José Miguel Hernández-Lobato, Robert Adolf, Michael A. Gelbart, Paul N. Whatmough, Gu-Yeon Wei, David M. Brooks:
A case for efficient accelerator design space exploration via Bayesian optimization. ISLPED 2017: 1-6 - [c7]Brandon Reagen, Yakun Sophia Shao, Sam Likun Xi, Gu-Yeon Wei, David Brooks:
Methods and infrastructure in the era of accelerator-centric architectures. MWSCAS 2017: 902-905 - [i2]Brandon Reagen, Udit Gupta, Robert Adolf, Michael M. Mitzenmacher, Alexander M. Rush, Gu-Yeon Wei, David M. Brooks:
Weightless: Lossy Weight Encoding For Deep Neural Network Compression. CoRR abs/1711.04686 (2017) - 2016
- [c6]Robert Adolf, Saketh Rama, Brandon Reagen, Gu-Yeon Wei, David M. Brooks:
Fathom: reference workloads for modern deep learning methods. IISWC 2016: 148-157 - [c5]Brandon Reagen, Paul N. Whatmough, Robert Adolf, Saketh Rama, Hyunkwang Lee, Sae Kyu Lee, José Miguel Hernández-Lobato, Gu-Yeon Wei, David M. Brooks:
Minerva: Enabling Low-Power, Highly-Accurate Deep Neural Network Accelerators. ISCA 2016: 267-278 - [i1]Robert Adolf, Saketh Rama, Brandon Reagen, Gu-Yeon Wei, David M. Brooks:
Fathom: Reference Workloads for Modern Deep Learning Methods. CoRR abs/1608.06581 (2016) - 2015
- [j1]Yakun Sophia Shao, Brandon Reagen, Gu-Yeon Wei, David M. Brooks:
The Aladdin Approach to Accelerator Design and Modeling. IEEE Micro 35(3): 58-70 (2015) - [c4]Xuan Zhang, Mario Lok, Tao Tong, Simon Chaput, Sae Kyu Lee, Brandon Reagen, Hyunkwang Lee, David M. Brooks, Gu-Yeon Wei:
A multi-chip system optimized for insect-scale flapping-wing robots. VLSIC 2015: 152- - 2014
- [c3]Brandon Reagen, Robert Adolf, Yakun Sophia Shao, Gu-Yeon Wei, David M. Brooks:
MachSuite: Benchmarks for accelerator design and customized architectures. IISWC 2014: 110-119 - [c2]Yakun Sophia Shao, Brandon Reagen, Gu-Yeon Wei, David M. Brooks:
Aladdin: A pre-RTL, power-performance accelerator simulator enabling large design space exploration of customized architectures. ISCA 2014: 97-108 - 2013
- [c1]Brandon Reagen, Yakun Sophia Shao, Gu-Yeon Wei, David M. Brooks:
Quantifying acceleration: Power/performance trade-offs of application kernels in hardware. ISLPED 2013: 395-400
Coauthor Index
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