How Computing Hardware Drives AI Progress

The evolution of computing hardware has played a significant role in driving progress in Artificial Intelligence (AI) and Machine Learning (ML). Graphics Processing Units (GPUs), initially designed for rendering graphics in video games, have become game-changers due to their parallel processing capabilities that can be harnessed to accelerate AI computations. The positive correlation between the development of new computing hardware and advancements in AI/ML is evident, with GPUs, Tensor Processing Units (TPUs), and other specialized hardware accelerating these computations. The backbone of training and inference in AI/ML systems relies on robust hardware architecture to handle large datasets efficiently. Specialized hardware like Apple's M1 and M2 chips, Nvidia's A100 GPUs, and Google's TPUs are optimized for matrix math and tensor operations, enabling faster iteration through batches of training data compared to general-purpose CPUs. New optimizations in hardware architecture include the use of analog in-memory computing, phase-change memory, memristors, and optical systems. Software frameworks like CUDA by Nvidia, Ray by Anyscale, and Caffe2 help deploy and scale models across different hardware backends. Choosing the right hardware infrastructure is crucial for determining the speed and efficiency of model training and deployment. Factors such as model complexity, overhead, latency bottlenecks, and off-the-shelf vs. custom training should be considered when selecting hardware. The current shortage of GPUs has led to a bottleneck in AI innovation progress, particularly for smaller startups and researchers. This scarcity affects not only cost but also development velocity, with major cloud service providers grappling with oversubscription for Nvidia's latest GPU offerings. The GPU gold rush is on, and access confers a competitive edge to those who can secure it.