While the CPU is the main processor, the GPU—originally developed to offload graphical processing and display tasks from the CPU—has evolved into the go-to technology for graphically and computationally intensive processing. Modern GPU devices contain thousands of cores capable of running multiple processes in parallel and are ideally suited for executing many smaller tasks at one time. They have been instrumental in supporting the proliferation of AI capabilities due to their ability to accelerate processing tasks. As with CPUs, GPU technology is advancing in two directions at once—with larger, more capable devices at the core of data centers, video editing systems, and AI model training, and with smaller, more efficient devices that enable deployment of powerful processing at the edge and in mobile and robotic systems.
In addition to the two main processor types, tensor processing units (TPUs) are rapidly gaining traction in applications that look to leverage machine learning. As the name implies, these processors are specifically designed for and tailored to perform tensor operations in support of neural network computations. TPUs are more power-efficient than GPUs, and because of their purpose-built design, they can execute training and inference tasks more quickly than their GPU counterparts. In addition, TPUs are integrated into the TensorFlow machine learning framework, lowering the barriers for developers who wish to leverage their capabilities. TPUs represent a promising processor technology that further enhances the performance capabilities of industrial computing systems used in AI applications. They are expected to grow and mature rapidly in support of the demands of those software developments.
PCIe Pushes Data Transfer Forward
Computing performance cannot advance on processor technology alone. Data needs to be able to flow from input devices into the system and from device to device within the system at rates sufficient to maximize the performance of available processors. Without this, the technological leaps and bounds are lost, and benefits are left unmaterialized. Fortunately, other core technologies central to computing systems have also been evolving to support the greater ecosystem. Data bus, memory, and storage technologies are leading the charge to enable faster, more efficient, and higher overall system performance. These elements—when combined with cutting-edge processing—create the environment necessary to foster accelerated growth of AI, robotics, machine vision, mobile computing, and edge computing.
The Peripheral Component Interconnect Express (PCIe) bus is the infrastructure roadway for almost all data in a PC. From its groundbreaking introduction nearly 20 years ago to today, it continues to be the bedrock of many computing platforms and facilitates the rapid transfer of data within a system. The most recent version, PCIe 6.0, was announced in 2021 and provides a total bidirectional bandwidth of 256 GB/s. This ensures that data can move at rates sufficient to keep modern memory and processors utilized for maximum overall productivity. PCIe 6.0 is a major boost for cloud computing, AI, and machine learning applications, as these need reliable and robust interconnectivity and performance while routinely placing intense data transfer and processing demands on a system. By enabling faster access and transfer and by minimizing idle times, PCI 6.0 also reduces inference and training times.