Brandon Reagen

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 71

Top Topics

Bayesian Inference

Homomorphic Encryption

Top Venues

Publications

Negar Neda, Austin Ebel, Benedict Reynwar, Brandon Reagen
CiFlow: Dataflow Analysis and Optimization of Key Switching for Homomorphic Encryption. ISPASS (2024)
Juran Ding, Yuanzhe Liu, Lingbin Sun, Brandon Reagen
NTTSuite: Number Theoretic Transform Benchmarks for Accelerating Encrypted Computation. CoRR (2024)
Nandan Kumar Jha, Brandon Reagen
DeepReShape: Redesigning Neural Networks for Efficient Private Inference. Trans. Mach. Learn. Res. 2024 (2024)
Jianqiao Mo, Karthik Garimella, Negar Neda, Austin Ebel, Brandon Reagen
Towards Fast and Scalable Private Inference. CoRR (2023)
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 (2023)
Jianqiao Mo, Jayanth Gopinath, Brandon Reagen
HAAC: A Hardware-Software Co-Design to Accelerate Garbled Circuits. ISCA (2023)
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 (2023)
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 (2023)
David Bruce Cousins, Yuriy Polyakov, Ahmad Al Badawi, Matthew French, Andrew G. Schmidt, Ajey 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 (2023)
Karthik Garimella, Zahra Ghodsi, Nandan Kumar Jha, Siddharth Garg, Brandon Reagen
Characterizing and Optimizing End-to-End Systems for Private Inference. ASPLOS (3) (2023)
Nandan Kumar Jha, Brandon Reagen
DeepReShape: Redesigning Neural Networks for Efficient Private Inference. CoRR (2023)
Austin Ebel, Karthik Garimella, Brandon Reagen
Orion: A Fully Homomorphic Encryption Compiler for Private Deep Neural Network Inference. CoRR (2023)
Deepraj Soni, Mohammed Nabeel, Negar Neda, Ramesh Karri, Michail Maniatakos, Brandon Reagen
Quantifying the Overheads of Modular Multiplication. ISLPED (2023)
Negar Neda, Austin Ebel, Benedict Reynwar, Brandon Reagen
CiFlow: Dataflow Analysis and Optimization of Key Switching for Homomorphic Encryption. CoRR (2023)
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)
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)
David Bruce Cousins, Yuriy Polyakov, Ahmad Al Badawi, Matthew French, Andrew Schmidt, Ajey Jacob, Benedict Reynwar, Kellie Canida, Akhilesh R. Jaiswal, Clynn Mathew, Homer Gamil, Negar Neda, Deepraj Soni, Michail Maniatakos, Brandon Reagen
TREBUCHET: Fully Homomorphic Encryption Accelerator for Deep Computation. CoRR (2023)
Naren Dhyani, Jianqiao Mo, Minsu Cho, Ameya Joshi, Siddharth Garg, Brandon Reagen, Chinmay Hegde
PriViT: Vision Transformers for Fast Private Inference. CoRR (2023)
Jianqiao Mo, Karthik Garimella, Negar Neda, Austin Ebel, Brandon Reagen
Towards Fast and Scalable Private Inference. CF (2023)
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)
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)
Jianqiao Mo, Jayanth Gopinath, Brandon Reagen
A Garbled Circuit Accelerator for Arbitrary, Fast Privacy-Preserving Computation. CoRR (2022)
Wooseok Choi, Brandon Reagen, Gu-Yeon Wei, David Brooks
Impala: Low-Latency, Communication-Efficient Private Deep Learning Inference. CoRR (2022)
Minsu Cho, Zahra Ghodsi, Brandon Reagen, Siddharth Garg, Chinmay Hegde
Sphynx: A Deep Neural Network Design for Private Inference. IEEE Secur. Priv. 20 (5) (2022)
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 (2022)
Hsuan Hsiao, Vincent T. Lee, Brandon Reagen, Armin Alaghi
Homomorphically Encrypted Computation using Stochastic Encodings. CoRR (2022)
Minsu Cho, Ameya Joshi, Siddharth Garg, Brandon Reagen, Chinmay Hegde
Selective Network Linearization for Efficient Private Inference. CoRR (2022)
Minsu Cho, Ameya Joshi, Brandon Reagen, Siddharth Garg, Chinmay Hegde
Selective Network Linearization for Efficient Private Inference. ICML (2022)
Karthik Garimella, Zahra Ghodsi, Nandan Kumar Jha, Siddharth Garg, Brandon Reagen
Characterizing and Optimizing End-to-End Systems for Private Inference. CoRR (2022)
Zahra Ghodsi, Nandan Kumar Jha, Brandon Reagen, Siddharth Garg
Circa: Stochastic ReLUs for Private Deep Learning. NeurIPS (2021)
Nandan Kumar Jha, Zahra Ghodsi, Siddharth Garg, Brandon Reagen
DeepReDuce: ReLU Reduction for Fast Private Inference. ICML (2021)
Meghan Cowan, Deeksha Dangwal, Armin Alaghi, Caroline Trippel, Vincent T. Lee, Brandon Reagen
Porcupine: a synthesizing compiler for vectorized homomorphic encryption. PLDI (2021)
Nandan Kumar Jha, Zahra Ghodsi, Siddharth Garg, Brandon Reagen
DeepReDuce: ReLU Reduction for Fast Private Inference. CoRR (2021)
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 (2021)
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)
Meghan Cowan, Deeksha Dangwal, Armin Alaghi, Caroline Trippel, Vincent T. Lee, Brandon Reagen
Porcupine: A Synthesizing Compiler for Vectorized Homomorphic Encryption. CoRR (2021)
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)
Karthik Garimella, Nandan Kumar Jha, Brandon Reagen
Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning. CoRR (2021)
Minsu Cho, Zahra Ghodsi, Brandon Reagen, Siddharth Garg, Chinmay Hegde
Sphynx: ReLU-Efficient Network Design for Private Inference. CoRR (2021)
Zahra Ghodsi, Nandan Kumar Jha, Brandon Reagen, Siddharth Garg
Circa: Stochastic ReLUs for Private Deep Learning. CoRR (2021)
Karthik Garimella, Nandan Kumar Jha, Zahra Ghodsi, Siddharth Garg, Brandon Reagen
CryptoNite: Revealing the Pitfalls of End-to-End Private Inference at Scale. CoRR (2021)
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 (2021)
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)
Zahra Ghodsi, Akshaj Kumar Veldanda, Brandon Reagen, Siddharth Garg
CryptoNAS: Private Inference on a ReLU Budget. NeurIPS (2020)
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)
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)
Zahra Ghodsi, Akshaj Kumar Veldanda, Brandon Reagen, Siddharth Garg
CryptoNAS: Private Inference on a ReLU Budget. CoRR (2020)
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)
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 (2020)
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 (2020)
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)
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)
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 (2019)
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 (2019)
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)
Yu Emma Wang, Yuhao Zhu, Glenn G. Ko, Brandon Reagen, Gu-Yeon Wei, David Brooks
Demystifying Bayesian Inference Workloads. ISPASS (2019)
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)
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)
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)
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 (10) (2018)
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)
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)
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 (2017)
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)
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) (2017)
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)
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) (2017)
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)
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)
Robert Adolf, Saketh Rama, Brandon Reagen, Gu-Yeon Wei, David M. Brooks
Fathom: reference workloads for modern deep learning methods. IISWC (2016)
Robert Adolf, Saketh Rama, Brandon Reagen, Gu-Yeon Wei, David M. Brooks
Fathom: Reference Workloads for Modern Deep Learning Methods. CoRR (2016)
Yakun Sophia Shao, Brandon Reagen, Gu-Yeon Wei, David M. Brooks
The Aladdin Approach to Accelerator Design and Modeling. IEEE Micro 35 (3) (2015)
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)
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)
Brandon Reagen, Robert Adolf, Yakun Sophia Shao, Gu-Yeon Wei, David M. Brooks
MachSuite: Benchmarks for accelerator design and customized architectures. IISWC (2014)
Brandon Reagen, Yakun Sophia Shao, Gu-Yeon Wei, David M. Brooks
Quantifying acceleration: Power/performance trade-offs of application kernels in hardware. ISLPED (2013)