Zobrazeno 1 - 10
of 61
pro vyhledávání: '"Xie, Mimi"'
Over-the-air (OTA) firmware updates are essential for updating and maintaining IoT devices, especially those batteryless devices reliant on energy harvesting power sources. Flash memory, favored for its low cost and high density, is extensively used
Externí odkaz:
http://arxiv.org/abs/2406.12189
Recent studies showed that Photoplethysmography (PPG) sensors embedded in wearable devices can estimate heart rate (HR) with high accuracy. However, despite of prior research efforts, applying PPG sensor based HR estimation to embedded devices still
Externí odkaz:
http://arxiv.org/abs/2303.13636
Heart Rate Variability (HRV) measures the variation of the time between consecutive heartbeats and is a major indicator of physical and mental health. Recent research has demonstrated that photoplethysmography (PPG) sensors can be used to infer HRV.
Externí odkaz:
http://arxiv.org/abs/2303.13637
Over-parameterization of deep neural networks (DNNs) has shown high prediction accuracy for many applications. Although effective, the large number of parameters hinders its popularity on resource-limited devices and has an outsize environmental impa
Externí odkaz:
http://arxiv.org/abs/2211.16667
IoT devices are increasingly being implemented with neural network models to enable smart applications. Energy harvesting (EH) technology that harvests energy from ambient environment is a promising alternative to batteries for powering those devices
Externí odkaz:
http://arxiv.org/abs/2207.09258
Autor:
Huang, Shaoyi, Liu, Ning, Liang, Yueying, Peng, Hongwu, Li, Hongjia, Xu, Dongkuan, Xie, Mimi, Ding, Caiwen
With the yearning for deep learning democratization, there are increasing demands to implement Transformer-based natural language processing (NLP) models on resource-constrained devices for low-latency and high accuracy. Existing BERT pruning methods
Externí odkaz:
http://arxiv.org/abs/2206.10461
Publikováno v:
In Journal of Systems Architecture September 2024 154
Energy harvesting technologies offer a promising solution to sustainably power an ever-growing number of Internet of Things (IoT) devices. However, due to the weak and transient natures of energy harvesting, IoT devices have to work intermittently re
Externí odkaz:
http://arxiv.org/abs/2203.11313
Energy harvesting (EH) IoT devices that operate intermittently without batteries, coupled with advances in deep neural networks (DNNs), have opened up new opportunities for enabling sustainable smart applications. Nevertheless, implementing those com
Externí odkaz:
http://arxiv.org/abs/2111.14051
Autor:
Huang, Shaoyi, Xu, Dongkuan, Yen, Ian E. H., Wang, Yijue, Chang, Sung-en, Li, Bingbing, Chen, Shiyang, Xie, Mimi, Rajasekaran, Sanguthevar, Liu, Hang, Ding, Caiwen
Conventional wisdom in pruning Transformer-based language models is that pruning reduces the model expressiveness and thus is more likely to underfit rather than overfit. However, under the trending pretrain-and-finetune paradigm, we postulate a coun
Externí odkaz:
http://arxiv.org/abs/2110.08190