Search Results for author:
Found 17 papers, 9 papers with code
Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS 2024 Challenge Survey
This paper introduces a novel benchmark as part of the AIS 2024 Real-Time Image Super-Resolution (RTSR) Challenge, which aims to upscale compressed images from 540p to 4K resolution (4x factor) in real-time on commercial GPUs.
Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Survey
This paper reviews the NTIRE 2024 RAW Image Super-Resolution Challenge, highlighting the proposed solutions and results.
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking.
NTIRE 2024 Challenge on Image Super-Resolution ($\times$4): Methods and Results
This paper reviews the NTIRE 2024 challenge on image super-resolution ($\times$4), highlighting the solutions proposed and the outcomes obtained.
Real-Time Image Segmentation via Hybrid Convolutional-Transformer Architecture Search
Manually replacing convolution layers with multi-head self-attention is non-trivial due to the costly overhead in memory to maintain high resolution.
Towards Cognitive AI Systems: a Survey and Prospective on Neuro-Symbolic AI
The remarkable advancements in artificial intelligence (AI), primarily driven by deep neural networks, have significantly impacted various aspects of our lives.
Swift Parameter-free Attention Network for Efficient Super-Resolution
To address this issue, we propose the Swift Parameter-free Attention Network (SPAN), a highly efficient SISR model that balances parameter count, inference speed, and image quality.
Ranked #37 on
Image Super-Resolution
on Set14 - 4x upscaling
GPT4AIGChip: Towards Next-Generation AI Accelerator Design Automation via Large Language Models
To our knowledge, this work is the first to demonstrate an effective pipeline for LLM-powered automated AI accelerator generation.
A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware
Graph neural networks (GNNs) are emerging for machine learning research on graph-structured data.
e-G2C: A 0.14-to-8.31 $μ$J/Inference NN-based Processor with Continuous On-chip Adaptation for Anomaly Detection and ECG Conversion from EGM
This work presents the first silicon-validated dedicated EGM-to-ECG (G2C) processor, dubbed e-G2C, featuring continuous lightweight anomaly detection, event-driven coarse/precise conversion, and on-chip adaptation.
DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks
Efficient deep neural network (DNN) models equipped with compact operators (e. g., depthwise convolutions) have shown great potential in reducing DNNs' theoretical complexity (e. g., the total number of weights/operations) while maintaining a decent model accuracy.
BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling
Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art method for graph-based learning tasks.
Ranked #1 on
Node Classification
on Reddit
PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication
Notably, little is known regarding the convergence rate of GCN training with both stale features and stale feature gradients.
Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?
In particular, recent works show that ViTs are more robust against adversarial attacks as compared with convolutional neural networks (CNNs), and conjecture that this is because ViTs focus more on capturing global interactions among different input/feature patches, leading to their improved robustness to local perturbations imposed by adversarial attacks.
A privacy-preserving distributed computational approach for distributed locational marginal prices
An important issue in today's electricity markets is the management of flexibilities offered by new practices, such as smart home appliances or electric vehicles.
BDS-GCN: Efficient Full-Graph Training of Graph Convolutional Nets with Partition-Parallelism and Boundary Sampling
While it can be natural to leverage graph partition and distributed training for tackling this challenge, this direction has only been slightly touched on previously due to the unique challenge posed by the GCN structures, especially the excessive amount of boundary nodes in each partitioned subgraph, which can easily explode the required memory and communications for distributed training of GCNs.
Sensor-Based Estimation of Dim Light Melatonin Onset (DLMO) Using Features of Two Time Scales
The results using data from 207 undergraduates show that our two-step model with two time-scale features has statistically significantly lower root-mean-square errors than models that use either daily sampled data or frequently sampled data.
