TY - GEN
T1 - Distilling the essence of raw video to reduce memory usage and energy at edge devices
AU - Zhang, Haibo
AU - Zhao, Shulin
AU - Pattnaik, Ashutosh
AU - Kandemir, Mahmut T.
AU - Sivasubramaniam, Anand
AU - Das, Chita R.
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/10/12
Y1 - 2019/10/12
N2 - Video broadcast and streaming are among the most widely used applications for edge devices. Roughly 82% of the mobile internet traffic is made up of video data. This is likely to worsen with the advent of 5G that will open up new opportunities for high resolution videos, virtual and augmented reality-based applications. The raw video data produced and consumed by edge devices is considerably higher than what is transmitted out of them. This leads to huge memory bandwidth and energy requirements from such edge devices. Therefore, optimizing the memory bandwidth and energy consumption needs is imperative for further improvements in energy efficiency of such edge devices. In this paper, we propose two mechanisms for on-the-fly compression and approximation of raw video data that is generated by the image sensors. The first mechanism, MidVB, performs lossless compression of the video frames coming out of the sensors and stores the compressed format into the memory. The second mechanism, Distill, builds on top of MidVB and further reduces memory consumption by approximating the video frame data. On an average, across 20 raw videos, MidVB and Distill are able to reduce the memory bandwidth by 43% and 72%, respectively, over the raw representation. They outperform a well known memory saving mechanism by 7% and 36%, respectively. Furthermore, MidVB and Distill reduce the energy consumption by 40% and 67%, respectively, over the baseline.
AB - Video broadcast and streaming are among the most widely used applications for edge devices. Roughly 82% of the mobile internet traffic is made up of video data. This is likely to worsen with the advent of 5G that will open up new opportunities for high resolution videos, virtual and augmented reality-based applications. The raw video data produced and consumed by edge devices is considerably higher than what is transmitted out of them. This leads to huge memory bandwidth and energy requirements from such edge devices. Therefore, optimizing the memory bandwidth and energy consumption needs is imperative for further improvements in energy efficiency of such edge devices. In this paper, we propose two mechanisms for on-the-fly compression and approximation of raw video data that is generated by the image sensors. The first mechanism, MidVB, performs lossless compression of the video frames coming out of the sensors and stores the compressed format into the memory. The second mechanism, Distill, builds on top of MidVB and further reduces memory consumption by approximating the video frame data. On an average, across 20 raw videos, MidVB and Distill are able to reduce the memory bandwidth by 43% and 72%, respectively, over the raw representation. They outperform a well known memory saving mechanism by 7% and 36%, respectively. Furthermore, MidVB and Distill reduce the energy consumption by 40% and 67%, respectively, over the baseline.
UR - http://www.scopus.com/inward/record.url?scp=85074451947&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85074451947&partnerID=8YFLogxK
U2 - 10.1145/3352460.3358298
DO - 10.1145/3352460.3358298
M3 - Conference contribution
AN - SCOPUS:85074451947
T3 - Proceedings of the Annual International Symposium on Microarchitecture, MICRO
SP - 657
EP - 669
BT - MICRO 2019 - 52nd Annual IEEE/ACM International Symposium on Microarchitecture, Proceedings
PB - IEEE Computer Society
T2 - 52nd Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2019
Y2 - 12 October 2019 through 16 October 2019
ER -