👉 Gun computing, also known as hardware-accelerated machine learning (HAML), refers to the use of specialized hardware designed to efficiently execute machine learning algorithms, particularly those for real-time applications like image and speech recognition. This hardware, often in the form of GPUs (Graphics Processing Units), TPUs (Tensor Processing Units), or FPGAs (Field-Programmable Gate Arrays), is optimized to perform the parallel computations required by deep learning models, significantly speeding up training and inference processes. Gun computing is particularly beneficial in scenarios where low latency and high throughput are critical, such as autonomous vehicles, smart security systems, and real-time translation services. By offloading the computationally intensive tasks from CPUs to these specialized devices, gun computing enables more complex and sophisticated AI applications to run on edge devices with limited processing power and memory.
