A Perspective on Test Methodologies for Supervised Machine Learning Accelerators

Seyedhamidreza Motaman, Swaroop Ghosh, Jongsun Park

Research output: Contribution to journalArticle

Abstract

Neural Network (NN) accelerators are essential in many emerging applications e.g., autonomous systems in making mission-critical decisions, health-care solutions to assist with diagnoses, etc. Any soft or hard failure during operation can potentially have catastrophic consequences in many of these applications. For instance, inaccurate classification during object recognition and tracking in autonomous vehicles can lead to crashes and subsequent injuries to the passengers. Therefore, testing Neural Network accelerators to ensure reliability and integrity of the underlying hardware is a crucial task to ensure the functionality, especially the ones that are used in mission-critical applications. Conventional functional, stuck-at and delay tests are not sufficient to characterize the ML systems since they face new test and validation challenges. This paper is aimed to provide a perspective on new test requirements and design for test techniques to cover ML features and detect various type of faults in NN accelerator. We discuss First-In-First-Out (FIFO) and Scratchpad based neural network hardware accelerators and propose test methods to detect the faults as well as fault location in different modules of the accelerator including MAC unit, Activation function module, and Processing Element (PE) registers.

Original languageEnglish (US)
Article number8790753
Pages (from-to)562-569
Number of pages8
JournalIEEE Journal on Emerging and Selected Topics in Circuits and Systems
Volume9
Issue number3
DOIs
StatePublished - Sep 1 2019

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Particle accelerators
Learning systems
Neural networks
Hardware
Electric fault location
Object recognition
Health care
Chemical activation
Testing
Processing

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

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A Perspective on Test Methodologies for Supervised Machine Learning Accelerators. / Motaman, Seyedhamidreza; Ghosh, Swaroop; Park, Jongsun.

In: IEEE Journal on Emerging and Selected Topics in Circuits and Systems, Vol. 9, No. 3, 8790753, 01.09.2019, p. 562-569.

Research output: Contribution to journalArticle

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