DeepReDuce: ReLU Reduction for Fast Private Inference

03/02/2021
by   Nandan Kumar Jha, et al.
0

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 target network, 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 3.5× (iso-accuracy), respectively.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/20/2023

DeepReShape: Redesigning Neural Networks for Efficient Private Inference

The increasing demand for privacy and security has driven the advancemen...
research
06/15/2020

CryptoNAS: Private Inference on a ReLU Budget

Machine learning as a service has given raise to privacy concerns surrou...
research
02/04/2022

Selective Network Linearization for Efficient Private Inference

Private inference (PI) enables inference directly on cryptographically s...
research
06/15/2021

Circa: Stochastic ReLUs for Private Deep Learning

The simultaneous rise of machine learning as a service and concerns over...
research
06/17/2021

Sphynx: ReLU-Efficient Network Design for Private Inference

The emergence of deep learning has been accompanied by privacy concerns ...
research
08/20/2023

AutoReP: Automatic ReLU Replacement for Fast Private Network Inference

The growth of the Machine-Learning-As-A-Service (MLaaS) market has highl...
research
07/14/2022

Characterizing and Optimizing End-to-End Systems for Private Inference

Increasing privacy concerns have given rise to Private Inference (PI). I...

Please sign up or login with your details

Forgot password? Click here to reset