👉 Amplifier computing, also known as neural network acceleration or hardware-accelerated AI computing, is a specialized approach to processing the vast amounts of data and complex computations required by artificial neural networks. It involves using custom-designed hardware, such as Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), or Field-Programmable Gate Arrays (FPGAs), to significantly speed up the training and inference phases of neural networks. These hardware solutions are optimized for parallel processing, which is crucial for handling the matrix multiplications and other linear algebra operations that are fundamental to neural network computations. By offloading these intensive tasks from general-purpose CPUs to specialized hardware, amplifier computing reduces latency, increases throughput, and ultimately enables the efficient deployment of sophisticated AI models in real-world applications.
