TY - JOUR
T1 - A Perspective on Test Methodologies for Supervised Machine Learning Accelerators
AU - Motaman, Seyedhamidreza
AU - Ghosh, Swaroop
AU - Park, Jongsun
N1 - Funding Information:
Manuscript received May 6, 2019; revised July 19, 2019; accepted August 2, 2019. Date of publication August 7, 2019; date of current version September 17, 2019. 2019. This work was supported in part by the Semiconductor Research Corporation under Grant 2847.001, in part by NSF under Grant CNS-1722557, Grant CNS-1814710, Grant CCF-1718474, Grant DGE-1723687, and Grant DGE-1821766, in part by the DARPA Young Faculty Award under Grant D15AP00089, and in part by the Industrial Strategic Technology Development Program under Grant 10077445. This article was recommended by Guest Editor R. Joshi. (Corresponding author: Seyedhamidreza Motaman.) S. Motaman is with Intel Inc., Santa Clara, CA 95054 USA (e-mail: sxm844@psu.edu).
Publisher Copyright:
© 2011 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - 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.
AB - 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.
KW - DFT
KW - hardware accelerator
KW - neural network
KW - stuck at fault
UR - http://www.scopus.com/inward/record.url?scp=85070720009&partnerID=8YFLogxK
U2 - 10.1109/JETCAS.2019.2933678
DO - 10.1109/JETCAS.2019.2933678
M3 - Article
AN - SCOPUS:85070720009
VL - 9
SP - 562
EP - 569
JO - IEEE Journal on Emerging and Selected Topics in Circuits and Systems
JF - IEEE Journal on Emerging and Selected Topics in Circuits and Systems
SN - 2156-3357
IS - 3
M1 - 8790753
ER -