87 citations · 219 across the 19 of their papers we have counts for
19 papers
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…
CHARM: Composing Heterogeneous Accelerators for Matrix Multiply on Versal ACAP Architecture
Jinming Zhuang, Jason Lau, Hanchen Ye +10
Dense matrix multiply (MM) serves as one of the most heavily used kernels in deep learning applications. To cope with the high computation demands of these applications, heterogene…
Sustainable AI Processing at the Edge
Sébastien Ollivier, Sheng Li, Yue Tang +5
Edge computing is a popular target for accelerating machine learning algorithms supporting mobile devices without requiring the communication latencies to handle them in the cloud.…
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…
FPIRM: Floating-point Processing in Racetrack Memories
Sébastien Ollivier, Xinyi Zhang, Yue Tang +3
Convolutional neural networks (CNN) have become a ubiquitous algorithm with growing applications in mobile and edge settings. We describe a compute-in-memory (CIM) technique called…
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…
Towards Cardiac Intervention Assistance: Hardware-aware Neural Architecture Exploration for Real-Time 3D Cardiac Cine MRI Segmentation
Dewen Zeng, Weiwen Jiang, Tianchen Wang +6
Real-time cardiac magnetic resonance imaging (MRI) plays an increasingly important role in guiding various cardiac interventions. In order to provide better visual assistance, the…
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…
Low Overhead Online Data Flow Tracking for Intermittently Powered Non-volatile FPGAs
Xinyi Zhang, Clay Patterson, Yongpan Liu +3
Energy harvesting is an attractive way to power future IoT devices since it can eliminate the need for battery or power cables. However, harvested energy is intrinsically unstable.…
Device-Circuit-Architecture Co-Exploration for Computing-in-Memory Neural Accelerators
Weiwen Jiang, Qiuwen Lou, Zheyu Yan +4
Co-exploration of neural architectures and hardware design is promising to simultaneously optimize network accuracy and hardware efficiency. However, state-of-the-art neural archit…
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…
Achieving Super-Linear Speedup across Multi-FPGA for Real-Time DNN Inference
Weiwen Jiang, Edwin H. -M. Sha, Xinyi Zhang +4
Real-time Deep Neural Network (DNN) inference with low-latency requirement has become increasingly important for numerous applications in both cloud computing (e.g., Apple's Siri)…
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…
Energy Harvesting Powered Embedded Systems
Yawen Wu, Zhenge Jia, Jingtong Hu
Historically, battery is the power source for mobile, embedded and remote system applications. However, the development of battery techniques does not follow the Moore's Law. The l…
Accuracy vs. Efficiency: Achieving Both through FPGA-Implementation Aware Neural Architecture Search
Weiwen Jiang, Xinyi Zhang, Edwin H. -M. Sha +4
A fundamental question lies in almost every application of deep neural networks: what is the optimal neural architecture given a specific dataset? Recently, several Neural Architec…
DAC-SDC Low Power Object Detection Challenge for UAV Applications
Xiaowei Xu, Xinyi Zhang, Bei Yu +4
The 55th Design Automation Conference (DAC) held its first System Design Contest (SDC) in 2018. SDC'18 features a lower power object detection challenge (LPODC) on designing and im…
An Area and Energy Efficient Design of Domain-Wall Memory-Based Deep Convolutional Neural Networks using Stochastic Computing
Xiaolong Ma, Yipeng Zhang, Geng Yuan +5
With recent trend of wearable devices and Internet of Things (IoTs), it becomes attractive to develop hardware-based deep convolutional neural networks (DCNNs) for embedded applica…