Accepted as NeurIPS 2020 regular paper! Abstract: Multiplication (e.g., convolution) is arguably a cornerstone of modern deep neural networks (DNNs). However, intensive multiplications cause expensive resource costs that challenge DNN deployment on resource-constrained edge devices, driving several attempts for multiplication-less deep networks.
Accepted as NeurIPS 2020 regular paper! Abstract: Multiplication (e.g., convolution) is arguably a cornerstone of modern deep neural networks (DNNs). However, intensive multiplications cause expensive resource costs that challenge DNN deployment on resource-constrained edge devices, driving several attempts for multiplication-less deep networks.
Accepted as NeurIPS 2020 regular paper! Abstract: Recent breakthroughs in deep neural networks (DNNs) have fueled a tremendous demand for intelligent edge devices featuring on-site learning, while the practical realization of such systems remains a challenge due to the limited resources available at the edge and the required massive training costs for state-of-the-art (SOTA) DNNs.
Accepted as NeurIPS 2020 regular paper! Abstract: Recent breakthroughs in deep neural networks (DNNs) have fueled a tremendous demand for intelligent edge devices featuring on-site learning, while the practical realization of such systems remains a challenge due to the limited resources available at the edge and the required massive training costs for state-of-the-art (SOTA) DNNs.
Accepted as ECCV 2020 regular paper! Abstract: There has been an explosive demand for bringing machine learning (ML) powered intelligence into numerous Internet-of-Things (IoT) devices. However, the effectiveness of such intelligent functionality requires in-situ continuous model adaptation for adapting to new data and environments, while the on-device computing and energy resources are usually extremely constrained.
Accepted as ECCV 2020 regular paper! Abstract: There has been an explosive demand for bringing machine learning (ML) powered intelligence into numerous Internet-of-Things (IoT) devices. However, the effectiveness of such intelligent functionality requires in-situ continuous model adaptation for adapting to new data and environments, while the on-device computing and energy resources are usually extremely constrained.