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Chemical Property-Guided Neural Networks for Naphtha Composition Prediction
The naphtha cracking process heavily relies on the composition of naphtha, which is a complex blend of different hydrocarbons. …
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FieldHAR: A Fully Integrated End-to-end RTL Framework for Human Activity Recognition with Neural Networks from Heterogeneous Sensors
In this work, we propose an open-source scalable end-to-end RTL framework FieldHAR, for complex human activity recognition (HAR) from …
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A Knowledge Distillation framework for Multi-Organ Segmentation of Medaka Fish in Tomographic Image
Morphological atlases are an important tool in organismal studies, and modern high-throughput Computed Tomography (CT) facilities can …
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On the Future of Training Spiking Neural Networks
Spiking Neural Networks have obtained a lot of attention in recent years due to their close depiction of brain functionality as well as …
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Optimization of DRAM based PIM Architecture for Energy-Efficient Deep Neural Network Training
Deep Neural Network (DNN) training consumes high-energy. On the other hand, DNNs deployed on edge devices demand very high-energy …
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