Can I read SESAME: Software defined Enclaves to Secure Inference Accelerators with Multi-tenant Execution on EtoBox?

SESAME: Software defined Enclaves to Secure Inference Accelerators with Multi-tenant Execution by Banerjee, Sarbartha; Ramrakhyani, Prakash; Wei, Shijia; Tiwari, Mohit is a scholarly article available to read on EtoBox.

What is SESAME: Software defined Enclaves to Secure Inference Accelerators with Multi-tenant Execution about?

Hardware-enclaves that target complex CPU designs compromise both security and performance. Programs have little control over micro-architecture, which leads to side-channel leaks, and then have to be transformed to have worst-case control- and data-flow behaviors and thus incur considerable slowdown. We propose to address these security and performance problems by bringing enclaves into the realm of accelerator-rich architectures. The key idea is to construct software-defined enclaves (SDEs) where the protections and slowdown are tied to an application-defined threat model and tuned by a compiler for the accelerator's specific domain. This vertically integrated approach requires new hardware data-structures to partition, clear, and shape the utilization of hardware resources; and a compiler that instantiates and schedules these data-structures to create multi-tenant enclaves on accelerators. We demonstrate our ideas with a comprehensive prototype -- Sesame -- that includes modifications to compiler, ISA, and microarchitecture to a decoupled access execute (DAE) accelerator framework for deep learning models. Our security evaluation shows that classifiers that could distinguish dif

Author

Banerjee, Sarbartha; Ramrakhyani, Prakash; Wei, Shijia; Tiwari, Mohit

Published

2020

Language

EN