87 citations · 809 across the 93 of their papers we have counts for
23 papers · 1 filter
Muffin: A Framework Toward Multi-Dimension AI Fairness by Uniting Off-the-Shelf Models
Yi Sheng, Junhuan Yang, Lei Yang +3
Model fairness (a.k.a., bias) has become one of the most critical problems in a wide range of AI applications. An unfair model in autonomous driving may cause a traffic accident if…
Proof-of-Federated-Learning-Subchain: Free Partner Selection Subchain Based on Federated Learning
Boyang Li, Bingyu Shen, Qing Lu +2
The continuous thriving of the Blockchain society motivates research in novel designs of schemes supporting cryptocurrencies. Previously multiple Proof-of-Deep-Learning(PoDL) conse…
On the Viability of using LLMs for SW/HW Co-Design: An Example in Designing CiM DNN Accelerators
Zheyu Yan, Yifan Qin, Xiaobo Sharon Hu +1
Deep Neural Networks (DNNs) have demonstrated impressive performance across a wide range of tasks. However, deploying DNNs on edge devices poses significant challenges due to strin…
NeFT: Negative Feedback Training to Improve Robustness of Compute-In-Memory DNN Accelerators
Yifan Qin, Zheyu Yan, Dailin Gan +5
Compute-in-memory accelerators built upon non-volatile memory devices excel in energy efficiency and latency when performing deep neural network (DNN) inference, thanks to their in…
Federated Self-Supervised Contrastive Learning and Masked Autoencoder for Dermatological Disease Diagnosis
Yawen Wu, Dewen Zeng, Zhepeng Wang +5
In dermatological disease diagnosis, the private data collected by mobile dermatology assistants exist on distributed mobile devices of patients. Federated learning (FL) can use de…
The Larger The Fairer? Small Neural Networks Can Achieve Fairness for Edge Devices
Yi Sheng, Junhuan Yang, Yawen Wu +5
Along with the progress of AI democratization, neural networks are being deployed more frequently in edge devices for a wide range of applications. Fairness concerns gradually emer…
SWIM: Selective Write-Verify for Computing-in-Memory Neural Accelerators
Zheyu Yan, Xiaobo Sharon Hu, Yiyu Shi
Computing-in-Memory architectures based on non-volatile emerging memories have demonstrated great potential for deep neural network (DNN) acceleration thanks to their high energy e…
Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learning
Yawen Wu, Dewen Zeng, Zhepeng Wang +5
Deep learning models have been deployed in an increasing number of edge and mobile devices to provide healthcare. These models rely on training with a tremendous amount of labeled…
One Proxy Device Is Enough for Hardware-Aware Neural Architecture Search
Bingqian Lu, Jianyi Yang, Weiwen Jiang +2
Convolutional neural networks (CNNs) are used in numerous real-world applications such as vision-based autonomous driving and video content analysis. To run CNN inference on variou…
Decentralized Unsupervised Learning of Visual Representations
Yawen Wu, Zhepeng Wang, Dewen Zeng +3
Collaborative learning enables distributed clients to learn a shared model for prediction while keeping the training data local on each client. However, existing collaborative lear…
RADARS: Memory Efficient Reinforcement Learning Aided Differentiable Neural Architecture Search
Zheyu Yan, Weiwen Jiang, Xiaobo Sharon Hu +1
Differentiable neural architecture search (DNAS) is known for its capacity in the automatic generation of superior neural networks. However, DNAS based methods suffer from memory u…
Enabling On-Device Self-Supervised Contrastive Learning With Selective Data Contrast
Yawen Wu, Zhepeng Wang, Dewen Zeng +2
After a model is deployed on edge devices, it is desirable for these devices to learn from unlabeled data to continuously improve accuracy. Contrastive learning has demonstrated it…
Dancing along Battery: Enabling Transformer with Run-time Reconfigurability on Mobile Devices
Yuhong Song, Weiwen Jiang, Bingbing Li +6
A pruning-based AutoML framework for run-time reconfigurability, namely RT3, is proposed in this work. This enables Transformer-based large Natural Language Processing (NLP) models…
Personalized Deep Learning for Ventricular Arrhythmias Detection on Medical IoT Systems
Zhenge Jia, Zhepeng Wang, Feng Hong +3
Life-threatening ventricular arrhythmias (VA) are the leading cause of sudden cardiac death (SCD), which is the most significant cause of natural death in the US. The implantable c…
Standing on the Shoulders of Giants: Hardware and Neural Architecture Co-Search with Hot Start
Weiwen Jiang, Lei Yang, Sakyasingha Dasgupta +2
Hardware and neural architecture co-search that automatically generates Artificial Intelligence (AI) solutions from a given dataset is promising to promote AI democratization; howe…
Enabling On-Device CNN Training by Self-Supervised Instance Filtering and Error Map Pruning
Yawen Wu, Zhepeng Wang, Yiyu Shi +1
This work aims to enable on-device training of convolutional neural networks (CNNs) by reducing the computation cost at training time. CNN models are usually trained on high-perfor…
Intermittent Inference with Nonuniformly Compressed Multi-Exit Neural Network for Energy Harvesting Powered Devices
Yawen Wu, Zhepeng Wang, Zhenge Jia +2
This work aims to enable persistent, event-driven sensing and decision capabilities for energy-harvesting (EH)-powered devices by deploying lightweight DNNs onto EH-powered devices…
Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Tasks
Lei Yang, Zheyu Yan, Meng Li +6
Neural Architecture Search (NAS) has demonstrated its power on various AI accelerating platforms such as Field Programmable Gate Arrays (FPGAs) and Graphic Processing Units (GPUs).…
On Neural Architecture Search for Resource-Constrained Hardware Platforms
Qing Lu, Weiwen Jiang, Xiaowei Xu +2
In the recent past, the success of Neural Architecture Search (NAS) has enabled researchers to broadly explore the design space using learning-based methods. Apart from finding bet…
When Single Event Upset Meets Deep Neural Networks: Observations, Explorations, and Remedies
Zheyu Yan, Yiyu Shi, Wang Liao +3
Deep Neural Network has proved its potential in various perception tasks and hence become an appealing option for interpretation and data processing in security sensitive systems.…
Hardware/Software Co-Exploration of Neural Architectures
Weiwen Jiang, Lei Yang, Edwin Sha +5
We propose a novel hardware and software co-exploration framework for efficient neural architecture search (NAS). Different from existing hardware-aware NAS which assumes a fixed h…
Revisiting the Evaluation of Uncertainty Estimation and Its Application to Explore Model Complexity-Uncertainty Trade-Off
Yukun Ding, Jinglan Liu, Jinjun Xiong +1
Accurately estimating uncertainties in neural network predictions is of great importance in building trusted DNNs-based models, and there is an increasing interest in providing acc…
On the Universal Approximability and Complexity Bounds of Quantized ReLU Neural Networks
Yukun Ding, Jinglan Liu, Jinjun Xiong +1
Compression is a key step to deploy large neural networks on resource-constrained platforms. As a popular compression technique, quantization constrains the number of distinct weig…