How to Achieve Real-Time AI/Edge Inference for Medical Imaging and Surveillance Applications
Deep learning is currently widely used in a variety of applications, including computer vision and natural language processing. End devices, such as smartphones and Internet-of-Things (IoT) sensors, are generating data that need to be analyzed in real-time using deep learning or used to train deep learning models. However, deep learning inference and training require substantial computation resources to run quickly.
In real-time applications like autonomous cars, a low latency should require to avoid the increase of risks like accidents because it should capture each movement and act along with the real-time.
Download this White Paper to know, how at Innominds, we came up with a promising approach to reduce the inference latency and computational cost in model compression.