TY - GEN
T1 - JPEG-2000 workload prediction for adaptive system on chip entropy coders architecture
AU - Chtourou, Sofien
AU - Hammami, Omar
AU - Chtourou, Mohamed
PY - 2006/1/1
Y1 - 2006/1/1
N2 - Multimedia applications are quickly becoming the most common workload for embedded systems and portable devices. Video, sound, image applications including digital TV, web access through wireless transmission are among the various possible tasks to be handled by next generation portable devices. In contrast with traditional workloads mostly static in nature, multimedia workloads exhibit high variability depending on the data processed which when coupled with real time processing requirements make difficult to dimension hardware resources when designing systems on chip. In this paper, we propose the use of a neural network for workload forecasting in order to adapt hardware resources during JPEG-2000 based image compression. The major performance bottleneck in JPEG-2000 being the entropy coder our aim is to adapt in real time the number of concurrent entropy coders through workload forecasting.
AB - Multimedia applications are quickly becoming the most common workload for embedded systems and portable devices. Video, sound, image applications including digital TV, web access through wireless transmission are among the various possible tasks to be handled by next generation portable devices. In contrast with traditional workloads mostly static in nature, multimedia workloads exhibit high variability depending on the data processed which when coupled with real time processing requirements make difficult to dimension hardware resources when designing systems on chip. In this paper, we propose the use of a neural network for workload forecasting in order to adapt hardware resources during JPEG-2000 based image compression. The major performance bottleneck in JPEG-2000 being the entropy coder our aim is to adapt in real time the number of concurrent entropy coders through workload forecasting.
UR - https://www.scopus.com/pages/publications/40649091622
U2 - 10.1109/ijcnn.2006.247188
DO - 10.1109/ijcnn.2006.247188
M3 - Conference contribution
AN - SCOPUS:40649091622
SN - 0780394909
SN - 9780780394902
T3 - IEEE International Conference on Neural Networks - Conference Proceedings
SP - 2807
EP - 2814
BT - International Joint Conference on Neural Networks 2006, IJCNN '06
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - International Joint Conference on Neural Networks 2006, IJCNN '06
Y2 - 16 July 2006 through 21 July 2006
ER -