AI-Inference

Edge AI: Running Intelligence Close to the User Edge AI means running AI tasks on devices or local servers that sit near the user, instead of sending every decision to a distant data center. When intelligence lives close to the user, apps respond faster, work offline when networks fail, and fewer details travel over the internet. Latency matters for real-time apps. Privacy matters for everyday data. Bandwidth matters for users with limited plans. Edge AI helps by processing data where it is created and only sharing results rather than raw data. ...

Edge AI: Running Intelligence at the Edge Edge AI means running intelligent software directly on devices near data sources—phones, cameras, sensors, and machines. This approach lets systems act quickly and locally, without waiting for signals to travel to a distant data center. It is a practical way to bring smart capabilities to everyday devices. The benefits are clear. Lower latency enables faster decisions, which helps safety, user experience, and real-time control. Privacy often improves because sensitive data can stay on the device instead of traveling over networks. It also reduces network bandwidth, since only relevant results or aggregates are shared rather than raw data. ...

Computer Vision and Speech Processing: Seeing and Hearing with AI Today, AI helps machines see and listen. Computer vision focuses on understanding what is in an image or video. Speech processing helps machines hear, transcribe, and interpret spoken language. These fields rely on large data, careful design, and clear goals. When they work together, devices can observe the world and respond in useful ways. What is computer vision? Computer vision uses cameras and sensors to capture scenes. It relies on machine learning models to detect objects, track motion, and read scenes. Tasks range from identifying a plant in a photo to describing an entire video. Simple tools can spot faces or logos, while advanced systems can map a street scene or help a robot move safely. ...

Hardware Accelerators: GPUs, TPUs and Beyond Hardware accelerators shape how we train and deploy modern AI. GPUs, TPUs and beyond offer different strengths for different tasks. This guide explains the main options and practical tips to choose the right tool for your workload. GPUs are built for parallel work. They have many cores, high memory bandwidth, and broad software support. They shine in training large models and in research where flexibility matters. With common frameworks like PyTorch and TensorFlow, you can use mixed precision to speed up training while keeping accuracy. In practice, a single GPU or a few can handle experiments quickly, and cloud options make it easy to scale up. ...