FPGAs

GPUs, TPUs, and FPGAs: Hardware Accelerators Explained Hardware accelerators are chips built to speed up specific tasks. They work with a traditional CPU to handle heavy workloads more efficiently. In data centers, on the cloud, and at the edge, GPUs, TPUs, and FPGAs are common choices for accelerating machine learning, graphics, and data processing. GPUs have many small cores that run in parallel. This design makes them very good at matrix math, image and video tasks, and training large neural networks. They come with mature software ecosystems, including libraries and tools that help developers optimize performance. The trade‑off is higher power use and a longer setup time for very specialized workloads. ...

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. ...