17 citations · 35 across the 29 of their papers we have counts for
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cs.DC2020
Runtime Deep Model Multiplexing for Reduced Latency and Energy Consumption Inference
Amir Erfan Eshratifar, Massoud Pedram
We propose a learning algorithm to design a light-weight neural multiplexer that given the input and computational resource requirements, calls the model that will consume the mini…
cs.DC2019
Energy-aware Scheduling of Jobs in Heterogeneous Cluster Systems Using Deep Reinforcement Learning
Amirhossein Esmaili, Massoud Pedram
Energy consumption is one of the most critical concerns in designing computing devices, ranging from portable embedded systems to computer cluster systems. Furthermore, in the past…