In this paper, we design ResPipe, a novel resilient model-distributed DNN training mechanism against delayed/failed workers. We analyze the communication cost ...
In this paper, we design ResPipe, a novel resilient model-distributed DNN training mechanism against de- layed/failed workers. We analyze the communication cost ...
To provide a certain level of fault tolerance, ResPipe [37] replicates the weights of a worker to its next i-th worker nodes to tolerate i's failures. However, ...
In this paper, we design ResPipe, a novel resilient model-distributed DNN training mechanism against delayed/failed workers. We analyze the communication cost ...
01/29/2021, Our paper on “ResPipe: Resilient Model-Distributed DNN Training at Edge Networks,” is accepted by IEEE ICASSP, 2021. 11/19/2020, Our paper on ...
Abstract—We consider a model-distributed learning framework in which layers of a deep learning model is distributed across multiple workers.
Respipe: Resilient model-distributed dnn training at edge networks. P Li, E Koyuncu, H Seferoglu. ICASSP 2021-2021 IEEE International Conference on Acoustics ...
In this paper, we design ResPipe, a novel resilient model-distributed DNN training mechanism against delayed/failed workers. We analyze the communication cost ...
In this paper, we analyze the potential of model-distributed inference in edge computing systems. Then, we develop an Adaptive and Resilient Model-Distributed ...
P. Li, E. Koyuncu, H. Seferoglu, "RESPIPE: Resilient model-distributed DNN training at edge networks," IEEE ICASSP, June 2021.
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