Nvidia accelerates the path to AI for IoT, hyperscale data centers

It’s safe to say the Internet of Things (IoT) era has arrived, as we live in a world where things are being connected at pace never seen before. Cars, video cameras, parking meters, building facilities and anything else one can think of are being connected to the internet, generating massive quantities of data.

The question is how does one interpret the data and understand what it means? Clearly trying to process this much data manually doesn’t work, which is why most of the web-scale companies have embraced artificial intelligence (AI) as a way to create new services that can leverage the data. This includes speech recognition, natural language processing, real-time translation, predictive services and contextual recommendations. Every major cloud provider and many large enterprises have AI initiatives underway.

+ Also on Network World: Nvidia GPU-powered autonomous car teaches itself to see and steer +

However, many data centers aren’t outfitted with enough processing power for AI inferencing. For those not familiar with the different phases of AI, training is teaching the AI new capabilities from an existing set of data. Inferencing is applying that learning to new data sets. Facebook’s image recognition and Amazon’s recommendation engine are both good examples of inferencing.

This week at its GPU Technology Conference (GTC) in China, Nvidia announced TensorRT 3, which promises to improve the performance and cut the cost of inferencing. TensorRT 3 takes very complex networks and optimizes and compiles them to get the best possible performance for AI inferencing. The below graphic shows that it acts as AI “middleware” so the data can be run through any framework and sent to any GPU. Recall this post where I explained why GPUs were much better for AI applications than CPUs. Nvidia has a wide range of GPUs, depending on the type of application and processing power required.

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