Serdab: An IoT Framework for Partitioning Neural Networks Computation across Multiple Enclaves

Tarek Elgamal, Klara Nahrstedt

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recent advances in Deep Neural Networks (DNN) and Edge Computing have made it possible to automatically analyze streams of videos from home/security cameras over hierarchical clusters that include edge devices, close to the video source, as well as remote cloud compute resources. However, preserving the privacy and confidentiality of users' sensitive data as it passes through different devices remains a concern to most users. Private user data is subject to attacks by malicious attackers or misuse by internal administrators who may use the data in activities that are not explicitly approved by the user. To address this challenge, we present Serdab, a distributed orchestration framework for deploying deep neural network computation across multiple secure enclaves (e.g., Intel SGX). Secure enclaves provide a guarantee on the privacy of the data/code deployed inside it. However, their limited hardware resources make them inefficient when solely running an entire deep neural network. To bridge this gap, Serdab presents a DNN partitioning strategy to distribute the layers of the neural network across multiple enclave devices or across an enclave device and other hardware accelerators. Our partitioning strategy achieves up to 4.7x speedup compared to executing the entire neural network in one enclave.

Original languageEnglish (US)
Title of host publicationProceedings - 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGRID 2020
EditorsLaurent Lefevre, Carlos A. Varela, George Pallis, Adel N. Toosi, Omer Rana, Rajkumar Buyya
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages519-528
Number of pages10
ISBN (Electronic)9781728160955
DOIs
StatePublished - May 2020
Event20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGRID 2020 - Melbourne, Australia
Duration: May 11 2020May 14 2020

Publication series

NameProceedings - 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGRID 2020

Conference

Conference20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGRID 2020
CountryAustralia
CityMelbourne
Period5/11/205/14/20

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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