They might want to use image classification and object detection algorithms for content moderation, text identification, and celebrity recognition. For example, many of the leading streaming media services have tens of thousands of hours of available content. Let us try to visualize the scope of the cost savings that can be realized through the 10-100X performance gains that can be realized through software AI acceleration. ![]() What’s more is that this AI performance boost driven by software optimizations is free, requiring almost no code changes or developer time and no additional hardware costs. While these hardware accelerators can deliver impressive AI performance improvements, software AI accelerators are required to deliver even higher orders of magnitude AI performance gains across deep learning, classical machine learning, and graph analytics, for the same hardware set-up. This has not only led to AI acceleration being incorporated into common chip architectures such as CPUs, GPUs, and FPGAs but also mushroomed a class of dedicated hardware AI accelerators specifically designed to accelerate artificial neural networks and machine learning applications. An equally important aspect of this AI growth equation is the ever-expanding demands it places on computer system requirements to deliver higher AI performance. The exponential growth of data has fed artificial intelligence’s voracious appetite and led to its transformation from niche to omnipresent.
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