TY - GEN
T1 - BIST Design and Implementation for a Fixed-Point Arithmetic MAC Unit within a Systolic Array
AU - Grout, Ian
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In embedded systems involving hardware accelerators to support embedded machine learning (ML) applications, both sequential and parallel operations are used to ensure the system can perform its required operations in the required time. For ML applications involving trained ML model inference, parallel (concurrent) hardware structures can be utilized to perform parallel multiplications usually involving matrix multiplication operations in hardware rather than software. In this paper, fault simulation, embedded instrument test access and built-in self-test (BIST) are considered in relation to the multiply-accumulate (MAC) unit that would be found within a systolic array. A fault simulation was performed on the pre-synthesis MAC unit Verilog HDL design module and used to develop a BIST unit accessible via an IEEE Std 1687 network. The design was prototyped using a Xilinx Artix-7 field programmable gate array (FPGA).
AB - In embedded systems involving hardware accelerators to support embedded machine learning (ML) applications, both sequential and parallel operations are used to ensure the system can perform its required operations in the required time. For ML applications involving trained ML model inference, parallel (concurrent) hardware structures can be utilized to perform parallel multiplications usually involving matrix multiplication operations in hardware rather than software. In this paper, fault simulation, embedded instrument test access and built-in self-test (BIST) are considered in relation to the multiply-accumulate (MAC) unit that would be found within a systolic array. A fault simulation was performed on the pre-synthesis MAC unit Verilog HDL design module and used to develop a BIST unit accessible via an IEEE Std 1687 network. The design was prototyped using a Xilinx Artix-7 field programmable gate array (FPGA).
KW - embedded instruments
KW - fault simulation
KW - FPGA
KW - MAC
KW - machine learning
KW - test
UR - http://www.scopus.com/inward/record.url?scp=85163003777&partnerID=8YFLogxK
U2 - 10.1109/iEECON56657.2023.10126602
DO - 10.1109/iEECON56657.2023.10126602
M3 - Conference contribution
AN - SCOPUS:85163003777
T3 - Proceeding - 2023 International Electrical Engineering Congress, iEECON 2023
SP - 113
EP - 116
BT - Proceeding - 2023 International Electrical Engineering Congress, iEECON 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Electrical Engineering Congress, iEECON 2023
Y2 - 8 March 2023 through 10 March 2023
ER -