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Processing Power Driving Practicality of Machine Learning

Mar. 02, 2018 – Despite their recent rise to prominence, the fundamentals of AI, specifically neural networks and deep learning, were established as far back as the late 50's and early 60's. The first neural network, the Perceptron, had a single layer and was good certain types of recognition. However, the Perceptron was unable to learn how to handle XOR operations. What eventually followed were multi-layer neural networks that performed much better at recognition tasks, but required more effort to train. Until the early 2000's the field was held back by limitations that can be tied back to insufficient computing resources and training data.

All this changed as chip speeds increased and the internet provided a rich set of images for use in training. ImageNet was one of the first really significant sources of labeled images, the type needed to perform higher quality training. Nevertheless, the theoretical underpinnings were established decades ago. Multilayer networks proved much more effective at recognition tasks, and with them came additional processing requirements. So today we have so called deep learning which boasts many layers of processing.

While neural networks provide a general-purpose method of solving problems that does not require formal coding, there are still many architectural choices that are needed to provide an optimal network for a given class of problems. Neural networks have relied on general purpose CPU's, GPU's or custom ASICs. CPU's have the advantage of flexibility, but this comes at the cost of lower throughput. Loading and storing of operands and results creates significant overhead. Likewise, GPU's are often optimized to use local memory and perform floating point operations, which together do not always best serve deep learning requirements.
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