87 citations · 219 across the 19 of their papers we have counts for
8 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…
H2H: Heterogeneous Model to Heterogeneous System Mapping with Computation and Communication Awareness
Xinyi Zhang, Cong Hao, Peipei Zhou +2
The complex nature of real-world problems calls for heterogeneity in both machine learning (ML) models and hardware systems. The heterogeneity in ML models comes from multi-sensor…
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…
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…
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…