Machine learning and quantum chemistry unite to simulate catalyst dynamics

Catalysts play an indispensable role in modern manufacturing. More than 80% of all manufactured products, from ...
Semiconductor Engineering

Deploying PyTorch Models On Edge Devices

AI is being rapidly adopted in edge computing. As a result, it is increasingly important to deploy machine learning models on Arm edge devices. Arm-based processors are common in embedded systems ...
TechCrunch

The hottest AI models, what they do, and how to use them

AI models are being cranked out at a dizzying pace, by everyone from Big Tech companies like Google to startups like OpenAI and Anthropic. Keeping track of the latest ones can be overwhelming. Adding ...
IEEE

A Real-time Detection Embedded Edge Computing System Based on SNN-ONNX Model Deployment Inference

Abstract: With the development of neural network technology, Spiking Neural Networks (SNNs) have shown great potential in edge computing and embedded systems due to their biologically inspired and low ...
GitHub

Blank output issue with CUDAExecutionProvider - Onnx Model Converted to fp16

When running a UNet segmentation model that has been converted to ONNX format with FP16 precision, using the CUDAExecutionProvider, the output appears to be blank. This issue is encountered during the ...
GitHub

Discrepancy in Outputs When Converting to ONNX using torch.autocast with float16

I converted a PyTorch model to ONNX inside torch.autocast(device_type="cpu", dtype=torch.float16) context. When running inference on both the original and converted models, I observed that the ...