GPUs In The Era of Artificial Intelligence

Graphical Processing Units was first developed in 1999 by NVidia and was referred to as the GeForce 256. This GPU model could process 10 million polygons per second and had more than 22 million transistors. The GeForce 256 was a single-chip processor with integrated transform, drawing and BitBLT support, lighting effects, triangle setup/clipping and rendering engines.

Conventionally, computing power is related to the quantity of CPUs and therefore the cores per processing unit. WinTel started to breach the enterprise data center, application performance and information throughput were directly proportional to the number of CPUs and available RAM. Whereas these factors are important to achieving the desired performance of enterprise applications, a new processor began to gain attention – Graphics Processing Unit or GPU.

But within the time of Machine Learning and AI, GPUs found a new place that makes them as applicable as CPUs. Deep learning, and advanced machine learning technique that is heavily utilized in AI and Cognitive Computing. Deep learning powers many synopses including autonomous cars, computer vision, cancer diagnosis, speech recognition, and many of alternative intelligent use cases.

GPUs became more popular as the requirement for graphics applications expanded. Eventually, they became not just an enhancement but a necessity for optimum performance of a computer. The designed logic chip enables the process of quick graphics and video exertion. Generally, the GPU is connected to the CPU and is completely distinctive from the motherboard. The random access memory (RAM) is connected through the accelerated graphics port (AGP) or the peripheral component interconnect express (PCI-Express) bus. Some GPUs are integrated into the northbridge on the motherboard and use the main memory as a digital storage area, but these GPUs are slower and have poorer performance.

Most GPUs use their transistors for 3-D computer graphics. However, some have increased memory for mapping vertices, equivalent to geographic information system (GIS) applications. Some of the more modern GPU technology supports programmable shaders implementing textures, mathematical vertices and accurate color formats. Applications like computer-aided design (CAD) can course over 200 billion operations per second and deliver up to seventeen million polygons per second. Many scientists and engineers use GPUs for additional in-depth calculated studies utilizing vector and matrix features.

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