Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are designed to partly emulate the functioning and structure of biological neural networks. As a ...

Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning ...

A new technical paper titled “Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems” was published by researchers at National ...

The results are really astonishing. Tests show that offices save up to 37% on their energy bills with AI-optimized and ...

A customizable KWS approach reduces resource demands and allows users to easily add their own keywords without machine ...

The NVIDIA Deep Learning Ambassador Workshop was held from June 17 to 24 at Sher-e-Kashmir University of Agricultural ...

Human DNA contains roughly 3 billion letters of genetic code. However, we understand only a fraction of what this vast ...

A demonstrative comparison between the proposed architecture and other discussed models in this paper is conducted. An outstanding competitive accuracy is achieved of 98.22% overall, 99% in detecting ...

Parkinson’s disease, the second most common neurodegenerative disorder globally, is often associated with vocal impairments, ...

Research team from Shandong University introduced MetaGIN, a lightweight deep learning framework for molecular property ...

The chip utilises brain-like technology to process sensor data on the device, reducing power consumption and enabling devices ...