Quantifying acceleration: Power/performance trade-offs of application kernels in hardware. - Brandon Reagen; Yakun Sophia Shao; Gu-Yeon Wei; David M. Brooks (ISLPED, 2013)

MachSuite: Benchmarks for accelerator design and customized architectures. - Brandon Reagen; Robert Adolf; Yakun Sophia Shao; Gu-Yeon Wei; David M. Brooks (IISWC, 2014)

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 (ISCA, 2014)

A multi-chip system optimized for insect-scale flapping-wing robots. - Xuan Zhang 001; Mario Lok; Tao Tong; Simon Chaput; Sae Kyu Lee; Brandon Reagen; Hyunkwang Lee; David M. Brooks; Gu-Yeon Wei (VLSIC, 2015)

The Aladdin Approach to Accelerator Design and Modeling. - Yakun Sophia Shao; Brandon Reagen; Gu-Yeon Wei; David M. Brooks (IEEE Micro, 2015)

Fathom: reference workloads for modern deep learning methods. - Robert Adolf; Saketh Rama; Brandon Reagen; Gu-Yeon Wei; David M. Brooks (IISWC, 2016)

Minerva: Enabling Low-Power, Highly-Accurate Deep Neural Network Accelerators. - Brandon Reagen; Paul N. Whatmough; Robert Adolf; Saketh Rama; Hyunkwang Lee; Sae Kyu Lee; José Miguel Hernández-Lobato; Gu-Yeon Wei; David M. Brooks (ISCA, 2016)

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 (ISCAS, 2017)

A case for efficient accelerator design space exploration via Bayesian optimization. - Brandon Reagen; José Miguel Hernández-Lobato; Robert Adolf; Michael A. Gelbart; Paul N. Whatmough; Gu-Yeon Wei; David M. Brooks (ISLPED, 2017)

Methods and infrastructure in the era of accelerator-centric architectures. - Brandon Reagen; Yakun Sophia Shao; Sam Likun Xi; Gu-Yeon Wei; David Brooks 001 (MWSCAS, 2017)

A Fully Integrated Battery-Powered System-on-Chip in 40-nm CMOS for Closed-Loop Control of Insect-Scale Pico-Aerial Vehicle. - Xuan Zhang 001; Mario Lok; Tao Tong; Sae Kyu Lee; Brandon Reagen; Simon Chaput; Pierre-Emile J. Duhamel; Robert J. Wood; David M. Brooks; Gu-Yeon Wei (IEEE J. Solid State Circuits, 2017)

Cognitive Computing Safety: The New Horizon for Reliability / The Design and Evolution of Deep Learning Workloads. - Yuhao Zhu 001; Vijay Janapa Reddi; Robert Adolf; Saketh Rama; Brandon Reagen; Gu-Yeon Wei; David M. Brooks (IEEE Micro, 2017)

Deep Learning for Computer Architects - Brandon Reagen; Robert Adolf; Paul N. Whatmough; Gu-Yeon Wei; David M. Brooks (Morgan & Claypool Publishers, 2017)

On-chip deep neural network storage with multi-level eNVM. - Marco Donato; Brandon Reagen; Lillian Pentecost; Udit Gupta; David Brooks 001; Gu-Yeon Wei (DAC, 2018)

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 (DAC, 2018)

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 001 (ICML, 2018)

Assisting High-Level Synthesis Improve SpMV Benchmark Through Dynamic Dependence Analysis. - Rafael Garibotti; Brandon Reagen; Yakun Sophia Shao; Gu-Yeon Wei; David M. Brooks (IEEE Trans. Circuits Syst. II Express Briefs, 2018)

MASR: A Modular Accelerator for Sparse RNNs. - Udit Gupta; Brandon Reagen; Lillian Pentecost; Marco Donato; Thierry Tambe; Alexander M. Rush; Gu-Yeon Wei; David Brooks 001 (PACT, 2019)

Machine Learning at Facebook: Understanding Inference at the Edge. - Carole-Jean Wu; David Brooks 001; Kevin Chen; Douglas Chen; Sy Choudhury; Marat Dukhan; Kim M. Hazelwood; Eldad Isaac; Yangqing Jia; Bill Jia; Tommer Leyvand; Hao Lu; et al. (HPCA, 2019)

Demystifying Bayesian Inference Workloads. - Yu Emma Wang; Yuhao Zhu 001; Glenn G. Ko; Brandon Reagen; Gu-Yeon Wei; David Brooks 001 (ISPASS, 2019)

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 001 (MICRO, 2019)

The Architectural Implications of Facebook's DNN-based Personalized Recommendation. - Udit Gupta; Xiaodong Wang 020; Maxim Naumov; Carole-Jean Wu; Brandon Reagen; David Brooks 001; Bradford Cottel; Kim M. Hazelwood; Bill Jia; Hsien-Hsin S. Lee; Andrey Malevich; Dheevatsa Mudigere; et al. (CoRR, 2019)

The Architectural Implications of Facebook's DNN-Based Personalized Recommendation. - Udit Gupta; Carole-Jean Wu; Xiaodong Wang 020; Maxim Naumov; Brandon Reagen; David Brooks 001; Bradford Cottel; Kim M. Hazelwood; Mark Hempstead; Bill Jia; Hsien-Hsin S. Lee; Andrey Malevich; et al. (HPCA, 2020)

DeepRecSys: A System for Optimizing End-To-End At-Scale Neural Recommendation Inference. - Udit Gupta; Samuel Hsia; Vikram Saraph; Xiaodong Wang 020; Brandon Reagen; Gu-Yeon Wei; Hsien-Hsin S. Lee; David Brooks 001; Carole-Jean Wu (ISCA, 2020)

RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing. - Liu Ke 001; Udit Gupta; Benjamin Youngjae Cho; David Brooks 001; Vikas Chandra; Utku Diril; Amin Firoozshahian; Kim M. Hazelwood; Bill Jia; Hsien-Hsin S. Lee; Meng Li 004; Bert Maher; et al. (ISCA, 2020)

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 (HASP@MICRO, 2020)

CryptoNAS: Private Inference on a ReLU Budget. - Zahra Ghodsi; Akshaj Kumar Veldanda; Brandon Reagen; Siddharth Garg (NeurIPS, 2020)

Cheetah: Optimizations and Methods for PrivacyPreserving Inference via Homomorphic Encryption. - Brandon Reagen; Wooseok Choi; Yeongil Ko; Vincent T. Lee; Gu-Yeon Wei; Hsien-Hsin S. Lee; David Brooks 001 (CoRR, 2020)

Mitigating Reverse Engineering Attacks on Local Feature Descriptors. - Deeksha Dangwal; Vincent T. Lee; Hyo Jin Kim 004; Tianwei Shen; Meghan Cowan; Rajvi Shah; Caroline Trippel; Brandon Reagen; Timothy Sherwood; Vasileios Balntas; Armin Alaghi; Eddy Ilg (BMVC, 2021)

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 001 (HPCA, 2021)

DeepReDuce: ReLU Reduction for Fast Private Inference. - Nandan Kumar Jha; Zahra Ghodsi; Siddharth Garg; Brandon Reagen (ICML, 2021)

Circa: Stochastic ReLUs for Private Deep Learning. - Zahra Ghodsi; Nandan Kumar Jha; Brandon Reagen; Siddharth Garg (NeurIPS, 2021)

Porcupine: a synthesizing compiler for vectorized homomorphic encryption. - Meghan Cowan; Deeksha Dangwal; Armin Alaghi; Caroline Trippel; Vincent T. Lee; Brandon Reagen (PLDI, 2021)

VIP-Bench: A Benchmark Suite for Evaluating Privacy-Enhanced Computation Frameworks. - Lauren Biernacki; Meron Zerihun Demissie; Kidus Birkayehu Workneh; Galane Basha Namomsa; Plato Gebremedhin; Fitsum Assamnew Andargie; Brandon Reagen; Todd M. Austin (SEED, 2021)

Analysis and Mitigations of Reverse Engineering Attacks on Local Feature Descriptors. - Deeksha Dangwal; Vincent T. Lee; Hyo Jin Kim 004; Tianwei Shen; Meghan Cowan; Rajvi Shah; Caroline Trippel; Brandon Reagen; Timothy Sherwood; Vasileios Balntas; Armin Alaghi; Eddy Ilg (CoRR, 2021)

Sphynx: ReLU-Efficient Network Design for Private Inference. - Minsu Cho; Zahra Ghodsi; Brandon Reagen; Siddharth Garg; Chinmay Hegde (CoRR, 2021)

Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning. - Karthik Garimella; Nandan Kumar Jha; Brandon Reagen (CoRR, 2021)

CryptoNite: Revealing the Pitfalls of End-to-End Private Inference at Scale. - Karthik Garimella; Nandan Kumar Jha; Zahra Ghodsi; Siddharth Garg; Brandon Reagen (CoRR, 2021)

Selective Network Linearization for Efficient Private Inference. - Minsu Cho; Ameya Joshi; Brandon Reagen; Siddharth Garg; Chinmay Hegde (ICML, 2022)

Homomorphically Encrypted Computation using Stochastic Encodings. - Hsuan Hsiao; Vincent T. Lee; Brandon Reagen; Armin Alaghi (CoRR, 2022)

Verifiable Access Control for Augmented Reality Localization and Mapping. - Shaowei Zhu; Hyo Jin Kim 004; Maurizio Monge; G. Edward Suh; Armin Alaghi; Brandon Reagen; Vincent T. Lee (CoRR, 2022)

Impala: Low-Latency, Communication-Efficient Private Deep Learning Inference. - Wooseok Choi; Brandon Reagen; Gu-Yeon Wei; David Brooks 001 (CoRR, 2022)

Sphynx: A Deep Neural Network Design for Private Inference. - Minsu Cho; Zahra Ghodsi; Brandon Reagen; Siddharth Garg; Chinmay Hegde (IEEE Secur. Priv., 2022)

Characterizing and Optimizing End-to-End Systems for Private Inference. - Karthik Garimella; Zahra Ghodsi; Nandan Kumar Jha; Siddharth Garg; Brandon Reagen (ASPLOS (3), 2023)

Towards Fast and Scalable Private Inference. - Jianqiao Mo; Karthik Garimella; Negar Neda; Austin Ebel; Brandon Reagen (CF, 2023)

Generating High-Performance Number Theoretic Transform Implementations for Vector Architectures. - Naifeng Zhang; Austin Ebel; Negar Neda; Patrick Brinich; Benedict Reynwar; Andrew G. Schmidt; Mike Franusich; Jeremy Johnson 001; Brandon Reagen; Franz Franchetti (HPEC, 2023)

HAAC: A Hardware-Software Co-Design to Accelerate Garbled Circuits. - Jianqiao Mo; Jayanth Gopinath; Brandon Reagen (ISCA, 2023)

Quantifying the Overheads of Modular Multiplication. - Deepraj Soni; Mohammed Nabeel 001; Negar Neda; Ramesh Karri; Michail Maniatakos; Brandon Reagen (ISLPED, 2023)

Exploring the Efficiency of Data-Oblivious Programs. - Lauren Biernacki; Biniyam Mengist Tiruye; Meron Zerihun Demissie; Fitsum Assamnew Andargie; Brandon Reagen; Todd M. Austin (ISPASS, 2023)

Design Space Exploration of Modular Multipliers for ASIC FHE accelerators. - Deepraj Soni; Mohammed Nabeel 001; Homer Gamil; Oleg Mazonka; Brandon Reagen; Ramesh Karri; Michail Maniatakos (ISQED, 2023)

Privacy Preserving In-memory Computing Engine. - Haoran Geng; Jianqiao Mo; Dayane Reis; Jonathan Takeshita; Taeho Jung; Brandon Reagen; Michael T. Niemier; Xiaobo Sharon Hu (CoRR, 2023)

PriViT: Vision Transformers for Fast Private Inference. - Naren Dhyani; Jianqiao Mo; Minsu Cho; Ameya Joshi; Siddharth Garg; Brandon Reagen; Chinmay Hegde (CoRR, 2023)

Orion: A Fully Homomorphic Encryption Compiler for Private Deep Neural Network Inference. - Austin Ebel; Karthik Garimella; Brandon Reagen (CoRR, 2023)

TREBUCHET: Fully Homomorphic Encryption Accelerator for Deep Computation. - 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; et al. (IACR Cryptol. ePrint Arch., 2023)

RPU: The Ring Processing Unit. - 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; et al. (IACR Cryptol. ePrint Arch., 2023)

SZKP: A Scalable Accelerator Architecture for Zero-Knowledge Proofs. - Alhad Daftardar; Brandon Reagen; Siddharth Garg (PACT, 2024)

CiFlow: Dataflow Analysis and Optimization of Key Switching for Homomorphic Encryption. - Negar Neda; Austin Ebel; Benedict Reynwar; Brandon Reagen (ISPASS, 2024)

NTTSuite: Number Theoretic Transform Benchmarks for Accelerating Encrypted Computation. - Juran Ding; Yuanzhe Liu; Lingbin Sun; Brandon Reagen (CoRR, 2024)

Osiris: A Systolic Approach to Accelerating Fully Homomorphic Encryption. - Austin Ebel; Brandon Reagen (CoRR, 2024)

ReLU's Revival: On the Entropic Overload in Normalization-Free Large Language Models. - Nandan Kumar Jha; Brandon Reagen (CoRR, 2024)

AERO: Softmax-Only LLMs for Efficient Private Inference. - Nandan Kumar Jha; Brandon Reagen (CoRR, 2024)

TruncFormer: Private LLM Inference Using Only Truncations. - Patrick Yubeaton; Jianqiao Mo; Karthik Garimella; Nandan Kumar Jha; Brandon Reagen; Chinmay Hegde; Siddharth Garg (CoRR, 2024)

PriViT: Vision Transformers for Private Inference. - Naren Dhyani; Jianqiao Cambridge Mo; Patrick Yubeaton; Minsu Cho; Ameya Joshi; Siddharth Garg; Brandon Reagen; Chinmay Hegde (Trans. Mach. Learn. Res., 2024)

DeepReShape: Redesigning Neural Networks for Efficient Private Inference. - Nandan Kumar Jha; Brandon Reagen (Trans. Mach. Learn. Res., 2024)

Orion: A Fully Homomorphic Encryption Framework for Deep Learning. - Austin Ebel; Karthik Garimella; Brandon Reagen (ASPLOS (2), 2025)

Need for zkSpeed: Accelerating HyperPlonk for Zero-Knowledge Proofs. - Alhad Daftardar; Jianqiao Mo; Joey Ah-kiow; Benedikt Bünz; Ramesh Karri; Siddharth Garg; Brandon Reagen (IACR Cryptol. ePrint Arch., 2025)

EinHops: Einsum Notation for Expressive Homomorphic Operations on RNS-CKKS Tensors. - Karthik Garimella; Austin Ebel; Brandon Reagen (IACR Cryptol. ePrint Arch., 2025)

ABLE: Optimizing Mixed Arithmetic and Boolean Garbled Circuit. - Jianqiao Cambridge Mo; Brandon Reagen (IACR Cryptol. ePrint Arch., 2025)

Entropy-Guided Attention for Private LLMs. - Nandan Kumar Jha; Brandon Reagen (CoRR, 2025)

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption. - Karthik Garimella; Austin Ebel; Gabrielle De Micheli; Brandon Reagen (CoRR, 2025)

A Random Matrix Theory Perspective on the Learning Dynamics of Multi-head Latent Attention. - Nandan Kumar Jha; Brandon Reagen (CoRR, 2025)

MTU: The Multifunction Tree Unit in zkSpeed for Accelerating HyperPlonk. - Jianqiao Mo; Alhad Daftardar; Joey Ah-kiow; Kaiyue Guo; Benedikt Bünz; Siddharth Garg; Brandon Reagen (CoRR, 2025)