TY - GEN
T1 - Predictability of image processing algorithms on heterogeneous MPSoC
AU - Paul, Johny
AU - Stechele, Walter
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/9
Y1 - 2015/12/9
N2 - Multiprocessor System-on-Chip (MPSoC) designs offer a lot of computational power assembled in a compact design. The computing power of MPSoCs can be further augmented by adding heterogeneous processing elements, e.g. massively parallel processor arrays (MPPA) and specialized hardware with instruction-set extensions. However, the presence of multiple processing elements (PEs) with different characteristics raises issues related to programming and application mapping. The conventional approach used for programming heterogeneous MPSoCs results in a static mapping of various parts of the application to different PE types, based on the nature of the algorithm and the structure of the PEs. Yet, such a mapping scheme independent of the instantaneous load on the PEs may lead to under-utilization of some type of PEs while overloading others. We investigate the benefits of a resource-aware programming model called Invasive Computing for dynamically mapping image processing applications to different types of PEs available on a heterogeneous MPSoC. A case study of visual object recognition is presented, including Harris corner detection and SIFT feature matching. Results indicate that resource-aware programming helps to predict the latency of the application program along with better overall workload distribution within the heterogeneous MPSoC.
AB - Multiprocessor System-on-Chip (MPSoC) designs offer a lot of computational power assembled in a compact design. The computing power of MPSoCs can be further augmented by adding heterogeneous processing elements, e.g. massively parallel processor arrays (MPPA) and specialized hardware with instruction-set extensions. However, the presence of multiple processing elements (PEs) with different characteristics raises issues related to programming and application mapping. The conventional approach used for programming heterogeneous MPSoCs results in a static mapping of various parts of the application to different PE types, based on the nature of the algorithm and the structure of the PEs. Yet, such a mapping scheme independent of the instantaneous load on the PEs may lead to under-utilization of some type of PEs while overloading others. We investigate the benefits of a resource-aware programming model called Invasive Computing for dynamically mapping image processing applications to different types of PEs available on a heterogeneous MPSoC. A case study of visual object recognition is presented, including Harris corner detection and SIFT feature matching. Results indicate that resource-aware programming helps to predict the latency of the application program along with better overall workload distribution within the heterogeneous MPSoC.
KW - Adaptation models
KW - Algorithm design and analysis
KW - Computer architecture
KW - Feature extraction
KW - Object recognition
KW - Programming
KW - System-on-chip
UR - http://www.scopus.com/inward/record.url?scp=84962283053&partnerID=8YFLogxK
U2 - 10.1109/ESTIMedia.2015.7351760
DO - 10.1109/ESTIMedia.2015.7351760
M3 - Conference contribution
AN - SCOPUS:84962283053
T3 - ESTIMedia 2015 - 13th IEEE Symposium on Embedded Systems for Real-Time Multimedia
BT - ESTIMedia 2015 - 13th IEEE Symposium on Embedded Systems for Real-Time Multimedia
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th IEEE Symposium on Embedded Systems for Real-Time Multimedia, ESTIMedia 2015
Y2 - 8 October 2015 through 9 October 2015
ER -