Search Results for author: Brandon Reagen

Found 14 papers, 6 papers with code

PriViT: Vision Transformers for Fast Private Inference

1 code implementation6 Oct 2023 Naren Dhyani, Jianqiao Mo, Minsu Cho, Ameya Joshi, Siddharth Garg, Brandon Reagen, Chinmay Hegde

The Vision Transformer (ViT) architecture has emerged as the backbone of choice for state-of-the-art deep models for computer vision applications.

Image Classification

Selective Network Linearization for Efficient Private Inference

1 code implementation4 Feb 2022 Minsu Cho, Ameya Joshi, Siddharth Garg, Brandon Reagen, Chinmay Hegde

To reduce PI latency we propose a gradient-based algorithm that selectively linearizes ReLUs while maintaining prediction accuracy.

Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning

2 code implementations26 Jul 2021 Karthik Garimella, Nandan Kumar Jha, Brandon Reagen

In this work, we ask: Is it feasible to substitute all ReLUs with low-degree polynomial activation functions for building deep, privacy-friendly neural networks?

Privacy Preserving Privacy Preserving Deep Learning

Sphynx: ReLU-Efficient Network Design for Private Inference

no code implementations17 Jun 2021 Minsu Cho, Zahra Ghodsi, Brandon Reagen, Siddharth Garg, Chinmay Hegde

The emergence of deep learning has been accompanied by privacy concerns surrounding users' data and service providers' models.

Circa: Stochastic ReLUs for Private Deep Learning

no code implementations NeurIPS 2021 Zahra Ghodsi, Nandan Kumar Jha, Brandon Reagen, Siddharth Garg

In this paper we re-think the ReLU computation and propose optimizations for PI tailored to properties of neural networks.

DeepReDuce: ReLU Reduction for Fast Private Inference

no code implementations2 Mar 2021 Nandan Kumar Jha, Zahra Ghodsi, Siddharth Garg, Brandon Reagen

This paper proposes DeepReDuce: a set of optimizations for the judicious removal of ReLUs to reduce private inference latency.

CryptoNAS: Private Inference on a ReLU Budget

no code implementations NeurIPS 2020 Zahra Ghodsi, Akshaj Veldanda, Brandon Reagen, Siddharth Garg

Machine learning as a service has given raise to privacy concerns surrounding clients' data and providers' models and has catalyzed research in private inference (PI): methods to process inferences without disclosing inputs.

DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference

no code implementations8 Jan 2020 Udit Gupta, Samuel Hsia, Vikram Saraph, Xiaodong Wang, Brandon Reagen, Gu-Yeon Wei, Hsien-Hsin S. Lee, David Brooks, Carole-Jean Wu

Neural personalized recommendation is the corner-stone of a wide collection of cloud services and products, constituting significant compute demand of the cloud infrastructure.

Distributed, Parallel, and Cluster Computing

MASR: A Modular Accelerator for Sparse RNNs

no code implementations23 Aug 2019 Udit Gupta, Brandon Reagen, Lillian Pentecost, Marco Donato, Thierry Tambe, Alexander M. Rush, Gu-Yeon Wei, David Brooks

The architecture is enhanced by a series of dynamic activation optimizations that enable compact storage, ensure no energy is wasted computing null operations, and maintain high MAC utilization for highly parallel accelerator designs.

speech-recognition Speech Recognition

Fathom: Reference Workloads for Modern Deep Learning Methods

1 code implementation23 Aug 2016 Robert Adolf, Saketh Rama, Brandon Reagen, Gu-Yeon Wei, David Brooks

Fathom has been released online, and this paper focuses on understanding the fundamental performance characteristics of each model.

Specificity

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