Using Deep Neural Networks (DNNs) in machine learning tasks is promising in delivering high-quality results but challenging to meet stringent latency requirements and energy constraints because of the memory-bound and the compute-bound execution pattern of DNNs.
The detection stage is a sequential labelling model based on BiLSTM-CRF and BERT contextual word representation.
The study explored a new technique of phonetic tone training, which may have a positive impact on second language learning and tone training.
In this paper, we propose Faith, an efficient framework for transformer verification on GPUs.
For irregularly sparse and fine-grained GNN workloads, such solutions miss the opportunity to jointly schedule/optimize the computation and communication operations for high-performance delivery.
Recently, graph neural networks (GNNs), as the backbone of graph-based machine learning, demonstrate great success in various domains (e. g., e-commerce).
It also brings profound impact to improve the applicability of the compute- and memory-intensive CNNs to a broad range of applications, such as mobile devices, which are generally short of computation power and memory.
Graph convolutional network (GCN) emerges as a promising direction to learn the inductive representation in graph data commonly used in widespread applications, such as E-commerce, social networks, and knowledge graphs.
Graph neural networks (GNNs) have achieved high performance in analyzing graph-structured data and have been widely deployed in safety-critical areas, such as finance and autonomous driving.
With the increasing popularity of graph-based learning, graph neural networks (GNNs) emerge as the essential tool for gaining insights from graphs.
To clear these hurdles, we propose 3D-Receptive Field (3DRF), an explainable and easy-to-compute metric, to estimate the quality of a CNN architecture and guide the search process of designs.
With the increasing popularity of graph-based learning, Graph Neural Networks (GNNs) win lots of attention from the research and industry field because of their high accuracy.
As the emerging trend of graph-based deep learning, Graph Neural Networks (GNNs) excel for their capability to generate high-quality node feature vectors (embeddings).
Distributed, Parallel, and Cluster Computing
As a promising solution to boost the performance of distance-related algorithms (e. g., K-means and KNN), FPGA-based acceleration attracts lots of attention, but also comes with numerous challenges.
Distributed, Parallel, and Cluster Computing Programming Languages