API World + AI DevWorld

This talk will provide deep insights in comparing CPU vs. GPU vs. Cloud based solutions in real-world scenarios. We will also explore how Open Source platforms can facilitate edge computing applications, including Java and Python frameworks' capabilities on ARM and X86 architectures.

As we continually search for bigger, more complicated problems, the demands we place on the computing power continue to grow rapidly. So where next - how can we continue to build models and algorithms that scale as our data does or enable us to tackle even more intricate problems?
Edge Processing not only optimizes large-scale data mining and aggregation, (by moving the data processing portion of an application to a single unit, known as the Gateway) but also facilitates Machine Learning in modern IoT applications. Most IoT gateways however, are only capable of running basic data aggregation functions due to their limited parallel processing capabilities of their CPUs.
For IoT devices to truly simulate human intelligence, not only do they need to implement neural networks, but they also need to take advantage of their distributed resources collectively.
In comparison with traditional CPU systems a GPU is capable of far higher peak performance for parallel data streams, which is a requirement of artificial neural networks. GPUs have evolved from a fixed pipeline graphics processing hardware into powerful programmable co-processing units capable of performing general purpose computing.

In this talk, Dr Mo Haghighi will explore the challenges and opportunities in edge computing, as well as discussing various platforms and their applicability for various real-world scenarios. This talk will explore JVM capabilities on ARM and X86 architectures, as well as large scale data mining on embedded GPUs. We will also present a benchmarking demo comparing CPUs vs. GPUs for processing time-series data, using a combination of Java and Python libraries.