The surge in artificial intelligence (AI) usage over the past two and a half years has dramatically changed not only software but hardware as well. As AI usage continues to evolve, PC makers have found in AI an opportunity to improve end-user devices by offering AI-specific hardware and marketing them as “AI PCs.”
Pre-AI hardware, adapted for AI
A few years ago, AI often depended on hardware that was not explicitly designed for AI. One example is graphics processors. Nvidia Graphics Processing Units (GPUs) are crucial in AI because they handle parallel processing efficiently, which is necessary for machine learning and deep learning. Their design enables simultaneous calculations, making them more effective than CPUs for AI model training and inference.
Another primary hardware type is the Field-Programmable Gate Array (FPGA) from Intel and other companies. An FPGA is an integrated circuit (IC) that can be reprogrammed multiple times. That flexibility makes it ideal for AI tasks. FPGAs accelerate deep learning and machine learning tasks. They provide hardware customization options that mimic the behavior of GPUs or ASICs.
FPGAs can be integrated with popular AI frameworks like TensorFlow and PyTorch using tools like the Intel FPGA AI Suite and the OpenVINO toolkit.
FPGAs are used across the automotive, healthcare and other industries. They are useful in edge computing scenarios where AI capabilities must be deployed close to the data source for faster decision-making and reduced latency.
And yet another type is Application-Specific Integrated Circuits (ASICs). One example is Google’s Tensor Processing Units (TPUs). TPUs are custom ASICs developed by Google ..
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