Brandon Reagen
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
33
Venues
15
Active years
2013–2026
Best venue rank
A*
Where they publish
Papers
33 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2026 | HPCA | zkPHIRE: A Programmable Accelerator for ZKPs over HIgh-degRee, Expressive Gates. | Alhad Daftardar, Jianqiao Mo, Joey Ah-kiow, Benedikt Bnz, Siddharth Garg, Brandon Reagen |
| 2025 | ACSSC | A Spatial Array for Spectrally Agile Wireless Processing. | Ali Rasteh, Andrew Hennessee, Ishaan Shivhare, Siddharth Garg, Sundeep Rangan, Brandon Reagen |
| 2025 | ASPLOS | Orion: A Fully Homomorphic Encryption Framework for Deep Learning. | Austin Ebel, Karthik Garimella, Brandon Reagen |
| 2025 | EMNLP | Spectral Scaling Laws in Language Models: emphHow Effectively Do Feed-Forward Networks Use Their Latent Space? | Nandan Kumar Jha, Brandon Reagen |
| 2025 | ICCAD | Network and Compiler Optimizations for Efficient Linear Algebra Kernels in Private Transformer Inference (Invited Paper). | Karthik Garimella, Negar Neda, Austin Ebel, Nandan Kumar Jha, Brandon Reagen |
| 2025 | ISCA | Need for zkSpeed: Accelerating HyperPlonk for Zero-Knowledge Proofs. | Alhad Daftardar, Jianqiao Mo, Joey Ah-kiow, Benedikt Bnz, Ramesh Karri, Siddharth Garg, Brandon Reagen |
| 2024 | ISPASS | CiFlow: Dataflow Analysis and Optimization of Key Switching for Homomorphic Encryption. | Negar Neda, Austin Ebel, Benedict Reynwar, Brandon Reagen |
| 2023 | ASPLOS | Characterizing and Optimizing End-to-End Systems for Private Inference. | Karthik Garimella, Zahra Ghodsi, Nandan Kumar Jha, Siddharth Garg, Brandon Reagen |
| 2023 | ISCA | HAAC: A Hardware-Software Co-Design to Accelerate Garbled Circuits. | Jianqiao Mo, Jayanth Gopinath, Brandon Reagen |
| 2023 | ISLPED | Quantifying the Overheads of Modular Multiplication. | Deepraj Soni, Mohammed Nabeel, Negar Neda, Ramesh Karri, Michail Maniatakos, Brandon Reagen |
| 2023 | ISPASS | Exploring the Efficiency of Data-Oblivious Programs. | Lauren Biernacki, Biniyam Mengist Tiruye, Meron Zerihun Demissie, Fitsum Assamnew Andargie, Brandon Reagen, Todd M. Austin |
| 2023 | ISPASS | RPU: The Ring Processing Unit. | 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 |
| 2022 | ICML | Selective Network Linearization for Efficient Private Inference. | Minsu Cho, Ameya Joshi, Brandon Reagen, Siddharth Garg, Chinmay Hegde |
| 2021 | BMVC | Mitigating Reverse Engineering Attacks on Local Feature Descriptors. | 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 |
| 2021 | HPCA | Cheetah: Optimizing and Accelerating Homomorphic Encryption for Private Inference. | Brandon Reagen, Wooseok Choi, Yeongil Ko, Vincent T. Lee, Hsien-Hsin S. Lee, Gu-Yeon Wei, David Brooks |
| 2021 | ICML | DeepReDuce: ReLU Reduction for Fast Private Inference. | Nandan Kumar Jha, Zahra Ghodsi, Siddharth Garg, Brandon Reagen |
| 2021 | PLDI | Porcupine: a synthesizing compiler for vectorized homomorphic encryption. | Meghan Cowan, Deeksha Dangwal, Armin Alaghi, Caroline Trippel, Vincent T. Lee, Brandon Reagen |
| 2020 | HPCA | The Architectural Implications of Facebook's DNN-Based Personalized Recommendation. | 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 |
| 2020 | ISCA | DeepRecSys: A System for Optimizing End-To-End At-Scale Neural Recommendation Inference. | Udit Gupta, Samuel Hsia, Vikram Saraph, Xiaodong Wang, Brandon Reagen, Gu-Yeon Wei, Hsien-Hsin S. Lee, David Brooks, Carole-Jean Wu |
| 2020 | ISCA | RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing. | 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 |
| 2020 | MICRO | SoK: Opportunities for Software-Hardware-Security Codesign for Next Generation Secure Computing. | Deeksha Dangwal, Meghan Cowan, Armin Alaghi, Vincent T. Lee, Brandon Reagen, Caroline Trippel |
| 2019 | HPCA | Machine Learning at Facebook: Understanding Inference at the Edge. | 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 |
| 2019 | ISPASS | Demystifying Bayesian Inference Workloads. | Yu Emma Wang, Yuhao Zhu, Glenn G. Ko, Brandon Reagen, Gu-Yeon Wei, David Brooks |
| 2019 | MICRO | MaxNVM: Maximizing DNN Storage Density and Inference Efficiency with Sparse Encoding and Error Mitigation. | Lillian Pentecost, Marco Donato, Brandon Reagen, Udit Gupta, Siming Ma, Gu-Yeon Wei, David Brooks |
| 2018 | DAC | On-chip deep neural network storage with multi-level eNVM. | Marco Donato, Brandon Reagen, Lillian Pentecost, Udit Gupta, David Brooks, Gu-Yeon Wei |
| 2018 | DAC | Ares: a framework for quantifying the resilience of deep neural networks. | Brandon Reagen, Udit Gupta, Lillian Pentecost, Paul N. Whatmough, Sae Kyu Lee, Niamh Mulholland, David M. Brooks, Gu-Yeon Wei |
| 2018 | ICLR | Weightless: Lossy weight encoding for deep neural network compression. | Brandon Reagen, Udit Gupta, Robert Adolf, Michael Mitzenmacher, Alexander M. Rush, Gu-Yeon Wei, David Brooks |
| 2018 | ICML | Weightless: Lossy weight encoding for deep neural network compression. | Brandon Reagen, Udit Gupta, Bob Adolf, Michael Mitzenmacher, Alexander M. Rush, Gu-Yeon Wei, David Brooks |
| 2017 | ISCAS | Using dynamic dependence analysis to improve the quality of high-level synthesis designs. | Rafael Garibotti, Brandon Reagen, Yakun Sophia Shao, Gu-Yeon Wei, David M. Brooks |
| 2017 | ISLPED | A case for efficient accelerator design space exploration via Bayesian optimization. | Brandon Reagen, Jos Miguel Hernndez-Lobato, Robert Adolf, Michael A. Gelbart, Paul N. Whatmough, Gu-Yeon Wei, David M. Brooks |
| 2016 | ISCA | Minerva: Enabling Low-Power, Highly-Accurate Deep Neural Network Accelerators. | Brandon Reagen, Paul N. Whatmough, Robert Adolf, Saketh Rama, Hyunkwang Lee, Sae Kyu Lee, Jos Miguel Hernndez-Lobato, Gu-Yeon Wei, David M. Brooks |
| 2014 | ISCA | Aladdin: A pre-RTL, power-performance accelerator simulator enabling large design space exploration of customized architectures. | Yakun Sophia Shao, Brandon Reagen, Gu-Yeon Wei, David M. Brooks |
| 2013 | ISLPED | Quantifying acceleration: Power/performance trade-offs of application kernels in hardware. | Brandon Reagen, Yakun Sophia Shao, Gu-Yeon Wei, David M. Brooks |