Author name cluster

Brandon Reagen

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

7 papers

2 author rows

TMLR Journal 2024 Journal Article

DeepReShape: Redesigning Neural Networks for Efficient Private Inference

Nandan Kumar Jha
Brandon Reagen

Prior work on Private Inference (PI)---inferences performed directly on encrypted input---has focused on minimizing a network's ReLUs, which have been assumed to dominate PI latency rather than FLOPs. Recent work has shown that FLOPs for PI can no longer be ignored and incur high latency penalties. In this paper, we develop DeepReShape, a technique that optimizes neural network architectures under PI's constraints, optimizing for both ReLUs {\em and} FLOPs for the first time. The key insight is strategically allocating channels to position the network's ReLUs in order of their criticality to network accuracy, simultaneously optimizes ReLU and FLOPs efficiency. DeepReShape automates network development with an efficient process, and we call generated networks HybReNets. We evaluate DeepReShape using standard PI benchmarks and demonstrate a 2.1% accuracy gain with a 5.2$\times$ runtime improvement at iso-ReLU on CIFAR-100 and an 8.7$\times$ runtime improvement at iso-accuracy on TinyImageNet. Furthermore, we investigate the significance of network selection in prior ReLU optimizations and shed light on the key network attributes for superior PI performance.

PDF Details

TMLR Journal 2024 Journal Article

PriViT: Vision Transformers for Private Inference

Naren Dhyani
Jianqiao Cambridge Mo
Patrick Yubeaton
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. However, ViTs are ill-suited for private inference using secure multi-party computation (MPC) protocols, due to the large number of non-polynomial operations (self-attention, feed-forward rectifiers, layer normalization). We develop PriViT, a gradient-based algorithm to selectively Taylorize nonlinearities in ViTs while maintaining their prediction accuracy. Our algorithm is conceptually very simple, easy to implement, and achieves improved performance over existing MPC-friendly transformer architectures in terms of the latency-accuracy Pareto frontier.

PDF Details

ICML Conference 2022 Conference Paper

Selective Network Linearization for Efficient Private Inference

Minsu Cho
Ameya Joshi
Brandon Reagen
Siddharth Garg
Chinmay Hegde

Private inference (PI) enables inferences directly on cryptographically secure data. While promising to address many privacy issues, it has seen limited use due to extreme runtimes. Unlike plaintext inference, where latency is dominated by FLOPs, in PI non-linear functions (namely ReLU) are the bottleneck. Thus, practical PI demands novel ReLU-aware optimizations. To reduce PI latency we propose a gradient-based algorithm that selectively linearizes ReLUs while maintaining prediction accuracy. We evaluate our algorithm on several standard PI benchmarks. The results demonstrate up to $4. 25%$ more accuracy (iso-ReLU count at 50K) or $2. 2\times$ less latency (iso-accuracy at 70%) than the current state of the art and advance the Pareto frontier across the latency-accuracy space. To complement empirical results, we present a “no free lunch" theorem that sheds light on how and when network linearization is possible while maintaining prediction accuracy.

Details

NeurIPS Conference 2021 Conference Paper

Circa: Stochastic ReLUs for Private Deep Learning

Zahra Ghodsi
Nandan Kumar Jha
Brandon Reagen
Siddharth Garg

The simultaneous rise of machine learning as a service and concerns over user privacy have increasingly motivated the need for private inference (PI). While recent work demonstrates PI is possible using cryptographic primitives, the computational overheads render it impractical. State-of-art deep networks are inadequate in this context because the source of slowdown in PI stems from the ReLU operations whereas optimizations for plaintext inference focus on reducing FLOPs. In this paper we re-think ReLU computations and propose optimizations for PI tailored to properties of neural networks. Specifically, we reformulate ReLU as an approximate sign test and introduce a novel truncation method for the sign test that significantly reduces the cost per ReLU. These optimizations result in a specific type of stochastic ReLU. The key observation is that the stochastic fault behavior is well suited for the fault-tolerant properties of neural network inference. Thus, we provide significant savings without impacting accuracy. We collectively call the optimizations Circa and demonstrate improvements of up to 4. 7$\times$ storage and 3$\times$ runtime over baseline implementations; we further show that Circa can be used on top of recent PI optimizations to obtain 1. 8$\times$ additional speedup.

PDF Details

ICML Conference 2021 Conference Paper

DeepReDuce: ReLU Reduction for Fast Private Inference

Nandan Kumar Jha
Zahra Ghodsi
Siddharth Garg
Brandon Reagen

The recent rise of privacy concerns has led researchers to devise methods for private neural inference—where inferences are made directly on encrypted data, never seeing inputs. The primary challenge facing private inference is that computing on encrypted data levies an impractically-high latency penalty, stemming mostly from non-linear operators like ReLU. Enabling practical and private inference requires new optimization methods that minimize network ReLU counts while preserving accuracy. This paper proposes DeepReDuce: a set of optimizations for the judicious removal of ReLUs to reduce private inference latency. The key insight is that not all ReLUs contribute equally to accuracy. We leverage this insight to drop, or remove, ReLUs from classic networks to significantly reduce inference latency and maintain high accuracy. Given a network architecture, DeepReDuce outputs a Pareto frontier of networks that tradeoff the number of ReLUs and accuracy. Compared to the state-of-the-art for private inference DeepReDuce improves accuracy and reduces ReLU count by up to 3. 5% (iso-ReLU count) and 3. 5x (iso-accuracy), respectively.

Details

NeurIPS Conference 2020 Conference Paper

CryptoNAS: Private Inference on a ReLU Budget

Zahra Ghodsi
Akshaj Kumar 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. Recently, researchers have adapted cryptographic techniques to show PI is possible, however all solutions increase inference latency beyond practical limits. This paper makes the observation that existing models are ill-suited for PI and proposes a novel NAS method, named CryptoNAS, for finding and tailoring models to the needs of PI. The key insight is that in PI operator latency cost are inverted: non-linear operations (e. g. , ReLU) dominate latency, while linear layers become effectively free. We develop the idea of a ReLU budget as a proxy for inference latency and use CryptoNAS to build models that maximize accuracy within a given budget. CryptoNAS improves accuracy by 3. 4% and latency by 2. 4x over the state-of-the-art.

PDF Details

ICML Conference 2018 Conference Paper

Weightless: Lossy weight encoding for deep neural network compression

Brandon Reagen
Udit Gupta 0001
Bob Adolf
Michael Mitzenmacher
Alexander M. Rush
Gu-Yeon Wei
David M. Brooks

The large memory requirements of deep neural networks limit their deployment and adoption on many devices. Model compression methods effectively reduce the memory requirements of these models, usually through applying transformations such as weight pruning or quantization. In this paper, we present a novel scheme for lossy weight encoding co-designed with weight simplification techniques. The encoding is based on the Bloomier filter, a probabilistic data structure that can save space at the cost of introducing random errors. Leveraging the ability of neural networks to tolerate these imperfections and by re-training around the errors, the proposed technique, named Weightless, can compress weights by up to 496x without loss of model accuracy. This results in up to a 1. 51x improvement over the state-of-the-art.

Details