TY - GEN
T1 - Robustly executing DNNs in IoT systems using coded distributed computing
AU - Hadidi, Ramyad
AU - Cao, Jiashen
AU - Ryoo, Michael S.
AU - Kim, Hyesoon
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/6/2
Y1 - 2019/6/2
N2 - Internet of Things (IoT) devices have access to an abundance of raw data for processing. With deep neural networks (DNNs), not only the demand for the computing power of IoT devices is increasing, but also privacy concerns are motivating the importance of close-toedge computation. DNN execution by distributing its computation is common in IoT systems. However, managing unstable latencies in a network and intermittent failures are serious challenges. Our work provides robustness and close-to-zero recovery latency by adapting coded distributed computing (CDC). We analyze robust execution on a mesh of Raspberry Pis by studying four DNNs.
AB - Internet of Things (IoT) devices have access to an abundance of raw data for processing. With deep neural networks (DNNs), not only the demand for the computing power of IoT devices is increasing, but also privacy concerns are motivating the importance of close-toedge computation. DNN execution by distributing its computation is common in IoT systems. However, managing unstable latencies in a network and intermittent failures are serious challenges. Our work provides robustness and close-to-zero recovery latency by adapting coded distributed computing (CDC). We analyze robust execution on a mesh of Raspberry Pis by studying four DNNs.
UR - https://www.scopus.com/pages/publications/85067812372
U2 - 10.1145/3316781.3322474
DO - 10.1145/3316781.3322474
M3 - Conference contribution
AN - SCOPUS:85067812372
T3 - Proceedings - Design Automation Conference
BT - Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 56th Annual Design Automation Conference, DAC 2019
Y2 - 2 June 2019 through 6 June 2019
ER -