8 citations · 21 across the 10 of their papers we have counts for
10 papers
Efficient and Direct Inference of Heart Rate Variability using Both Signal Processing and Machine Learning
Yuntong Zhang, Jingye Xu, Mimi Xie +3
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 demonstra…
PPG-based Heart Rate Estimation with Efficient Sensor Sampling and Learning Models
Yuntong Zhang, Jingye Xu, Mimi Xie +4
Recent studies showed that Photoplethysmography (PPG) sensors embedded in wearable devices can estimate heart rate (HR) with high accuracy. However, despite of prior research effor…
Dynamic Sparse Training via Balancing the Exploration-Exploitation Trade-off
Shaoyi Huang, Bowen Lei, Dongkuan Xu +4
Over-parameterization of deep neural networks (DNNs) has shown high prediction accuracy for many applications. Although effective, the large number of parameters hinders its popula…
EVE: Environmental Adaptive Neural Network Models for Low-power Energy Harvesting System
Sahidul Islam, Shanglin Zhou, Ran Ran +4
IoT devices are increasingly being implemented with neural network models to enable smart applications. Energy harvesting (EH) technology that harvests energy from ambient environm…
An Automatic and Efficient BERT Pruning for Edge AI Systems
Shaoyi Huang, Ning Liu, Yueying Liang +5
With the yearning for deep learning democratization, there are increasing demands to implement Transformer-based natural language processing (NLP) models on resource-constrained de…
Energy Harvesting Aware Multi-hop Routing Policy in Distributed IoT System Based on Multi-agent Reinforcement Learning
Wen Zhang, Tao Liu, Mimi Xie +3
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 na…
Enabling Fast Deep Learning on Tiny Energy-Harvesting IoT Devices
Sahidul Islam, Jieren Deng, Shanglin Zhou +3
Energy harvesting (EH) IoT devices that operate intermittently without batteries, coupled with advances in deep neural networks (DNNs), have opened up new opportunities for enablin…
Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm
Shaoyi Huang, Dongkuan Xu, Ian E. H. Yen +8
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,…
Binary Complex Neural Network Acceleration on FPGA
Hongwu Peng, Shanglin Zhou, Scott Weitze +9
Being able to learn from complex data with phase information is imperative for many signal processing applications. Today' s real-valued deep neural networks (DNNs) have shown effi…
FTRANS: Energy-Efficient Acceleration of Transformers using FPGA
Bingbing Li, Santosh Pandey, Haowen Fang +7
In natural language processing (NLP), the "Transformer" architecture was proposed as the first transduction model replying entirely on self-attention mechanisms without using seque…