TY - GEN
T1 - Test Access Control for an Embedded MAC Unit Based Core using IEEE Std 1687
AU - Grout, Ian
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Ahstract-A major move today in developing and deploying embedded electronic systems is to include trained machine learning (ML) models that can be used for predicting new data in order to include added value to the embedded system functionality. The ML model can be implemented in software or hardware, and would typically require a highly parallelized architecture in order to reduce computation times. The design effort to create a suitable implementation is non-trivial. However, an aspect of the overall system development and deployment requirements that can be overlooked is the testability of the design hardware. In this paper, the test access for a trained ML model implemented in hardware as a MAC unit, and realized within a field programmable gate array (FPGA), is presented. The IEEE Std 1687 to provide access to embedded instrumentation is utilized to support the test requirements. The ML trained model is considered as an embedded computation core connected to a host processor.
AB - Ahstract-A major move today in developing and deploying embedded electronic systems is to include trained machine learning (ML) models that can be used for predicting new data in order to include added value to the embedded system functionality. The ML model can be implemented in software or hardware, and would typically require a highly parallelized architecture in order to reduce computation times. The design effort to create a suitable implementation is non-trivial. However, an aspect of the overall system development and deployment requirements that can be overlooked is the testability of the design hardware. In this paper, the test access for a trained ML model implemented in hardware as a MAC unit, and realized within a field programmable gate array (FPGA), is presented. The IEEE Std 1687 to provide access to embedded instrumentation is utilized to support the test requirements. The ML trained model is considered as an embedded computation core connected to a host processor.
KW - FPGA
KW - IEEE Std 1687
KW - MAC unit
KW - machine learning
KW - test
UR - http://www.scopus.com/inward/record.url?scp=85126658405&partnerID=8YFLogxK
U2 - 10.1109/ICPEI52436.2021.9690671
DO - 10.1109/ICPEI52436.2021.9690671
M3 - Conference contribution
AN - SCOPUS:85126658405
T3 - 2021 International Conference on Power, Energy and Innovations, ICPEI 2021
SP - 179
EP - 182
BT - 2021 International Conference on Power, Energy and Innovations, ICPEI 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 International Conference on Power, Energy and Innovations, ICPEI 2021
Y2 - 20 October 2021 through 22 October 2021
ER -