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Deep Partial Updating: Towards Communication Efficient Updating for On-device Inference
(2022)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2022Emerging edge intelligence applications require the server to retrain and update deep neural networks deployed on remote edge nodes to leverage newly collected data samples. Unfortunately, it may be impossible in practice to continuously send fully updated weights to these edge nodes due to the highly constrained communication resource. In this paper, we propose the weight-wise deep partial updating paradigm, which smartly selects a small ...Conference Paper -
Stitching Weight-Shared Deep Neural Networks for Efficient Multitask Inference on GPU
(2022)2022 19th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)Intelligent personal and home applications demand multiple deep neural networks (DNNs) running on resource-constrained platforms for compound inference tasks, known as multitask inference. To fit multiple DNNs into low-resource devices, emerging techniques resort to weight sharing among DNNs to reduce their storage. However, such reduction in storage fails to translate into efficient execution on common accelerators such as GPUs. Most DNN ...Conference Paper -
p-Meta: Towards On-device Deep Model Adaptation
(2022)KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data MiningConference Paper -
Accurate Onboard Predictions for Indoor Energy Harvesting using Random Forests
(2022)2022 11th Mediterranean Conference on Embedded Computing (MECO)Indoor energy harvesting has recently enabled long-term deployments of sustainable IoT sensor nodes. The performance of such systems operating in an energy-neutral manner can be optimized by exploiting energy prediction models. Numerous prediction algorithms have been developed, yet they are primarily intended for outdoor (solar) energy harvesting. Indoor environments are much more challenging to predict since the primary energy is very ...Conference Paper