Search Results for author:
Found 10 papers, 4 papers with code
SAE: Single Architecture Ensemble Neural Networks
Ensembles of separate neural networks (NNs) have shown superior accuracy and confidence calibration over single NN across tasks.
Sparse-DySta: Sparsity-Aware Dynamic and Static Scheduling for Sparse Multi-DNN Workloads
Running multiple deep neural networks (DNNs) in parallel has become an emerging workload in both edge devices, such as mobile phones where multiple tasks serve a single user for daily activities, and data centers, where various requests are raised from millions of users, as seen with large language models.
When Monte-Carlo Dropout Meets Multi-Exit: Optimizing Bayesian Neural Networks on FPGA
Bayesian Neural Networks (BayesNNs) have demonstrated their capability of providing calibrated prediction for safety-critical applications such as medical imaging and autonomous driving.
LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physics
This work presents a novel reconfigurable architecture for Low Latency Graph Neural Network (LL-GNN) designs for particle detectors, delivering unprecedented low latency performance.
Adaptable Butterfly Accelerator for Attention-based NNs via Hardware and Algorithm Co-design
By jointly optimizing the algorithm and hardware, our FPGA-based butterfly accelerator achieves 14. 2 to 23. 2 times speedup over state-of-the-art accelerators normalized to the same computational budget.
Algorithm and Hardware Co-design for Reconfigurable CNN Accelerator
Recent advances in algorithm-hardware co-design for deep neural networks (DNNs) have demonstrated their potential in automatically designing neural architectures and hardware designs.
Optimizing Bayesian Recurrent Neural Networks on an FPGA-based Accelerator
To further improve the overall algorithmic-hardware performance, a co-design framework is proposed to explore the most fitting algorithmic-hardware configurations for Bayesian RNNs.
High-Performance FPGA-based Accelerator for Bayesian Neural Networks
Neural networks (NNs) have demonstrated their potential in a wide range of applications such as image recognition, decision making or recommendation systems.
ComBiNet: Compact Convolutional Bayesian Neural Network for Image Segmentation
Fully convolutional U-shaped neural networks have largely been the dominant approach for pixel-wise image segmentation.
VINNAS: Variational Inference-based Neural Network Architecture Search
In recent years, neural architecture search (NAS) has received intensive scientific and industrial interest due to its capability of finding a neural architecture with high accuracy for various artificial intelligence tasks such as image classification or object detection.
