One significant development in modern computing with regard to machine learning and AI is the diversification of processing and processor chips utilized. In the past, general processors were expected to handle a wide range of tasks. However, it was soon realized that adding millions of transistors for every purpose led to inefficiency, high prices, and increased power consumption. This resulted in the adoption of purpose-built processors specialized in handling specific compute tasks with high performance and power efficiency. The use of RISC processors, such as the Arm architecture, was an early example of this trend.
Moreover, as demands for modern computing increased, new processing units were developed to work alongside general-purpose CPUs, offloading specific tasks that CPUs were unable to handle alone. For instance, GPUs were introduced to manage the math and data manipulation required for tasks like computer graphics and digital visualization. With the rise of machine learning and AI workloads, GPUs have become popular for handling the complex math and data processing involved in these tasks. Additionally, other specialized processing units like TPUs, which focus on math tasks such as matrix multiplication, and NPUs, which mimic human brain neural networks for accelerated AI tasks, have emerged to support ML and AI applications in the enterprise.
When it comes to understanding the roles of CPUs and GPUs in AI, CPUs are the backbone of computers and play an essential role in executing general operations. GPUs, on the other hand, are highly specialized devices designed for specific tasks at a large scale, providing a high level of parallelism in mathematical operations like matrix and vector computations. While CPUs can perform complex operations involved in ML and AI, they are limited in handling simultaneous high-volume complex tasks, making GPUs a key component in ML and AI platforms where parallel processing is required.
On the other hand, TPUs are ASICs designed specifically for handling vast amounts of parallel mathematical tasks in ML and AI workloads. With integrated matrix multiply units, TPUs are cost-effective and well-suited for tasks like training deep learning models and inference systems. NPUs, another type of ASIC, focus on accelerating specific AI tasks that rely on inference rather than training. They are commonly found in edge and mobile devices, providing faster processing for tasks like facial recognition or speech translation.
In conclusion, the choice of processing unit for ML and AI tasks depends on the project’s specific requirements. While CPUs can handle basic tasks, GPUs excel in handling parallel mathematical operations efficiently, TPUs offer increased specialization, and NPUs are ideal for inference-based tasks. Each processing unit plays a vital role in the development and deployment of ML and AI applications.