Module C 100 AI hardware 13 06 26 Autosaved

The historical trajectory of AI hardware mirrors the increasing complexity of machine learning applications. Initially, (1) central processing units (CPUs) dominated computational workloads, providing adequate performance for general-purpose tasks. However, the rise of deep learning revealed the limitations of the parallelism required by neural networks. This realization spurred the widespread adoption of (2) graphics processing units (GPUs), which became essential for training deep learning models due to their architectural focus on parallel computation.
In subsequent years, the demand for enhanced computational efficiency drove the development of (3 )application-specific integrated circuits (ASICs) and (4) neural processing units (NPUs), both created to optimize specific AI tasks. These innovations have fundamentally transformed the hardware landscape, facilitating advancements in natural language processing, computer vision, and reinforcement learning.An ASIC (Application-Specific Integrated Circuit) is a custom-designed chip built for a specific task. A TPU (Tensor Processing Unit) is a type of ASIC developed by Google, specifically optimized for tensor-based machine learning workloads. While all TPUs are ASICs, not all ASICs are TPUs.FPGAs are reconfigurable chips that serve as AI accelerators, particularly in applications that demand low latency and parallel processing, such as computer vision and autonomous control systems. An AI accelerator is specialized hardware designed to improve the speed and efficiency of AI computations such as training or inference. These accelerators—such as GPUs, FPGAs, and ASICs—are essential for running complex neural networks in real-time, reducing power consumption, and offloading intensive tasks from general-purpose CPUs.

Видео Module C 100 AI hardware 13 06 26 Autosaved канала Veerendra Kumar