So why does it take so much computing power and time to teach AI? The problem is that modern neural networks like Google's DeepMind or IBM Watson must perform billions of tasks in in parallel. That requires numerous CPU memory calls, which quickly adds up over billions of cycles. The researchers debated using new storage tech like resistive RAM that can permanently store data with DRAM-like speeds. However, they eventually came up with the idea for a new type of chip called a resistive processing unit (RPU) that puts large amounts of resistive RAM directly onto a CPU.

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Such chips could fetch the data as quickly as they can process it, dramatically decreasing neural network training times and power required. "This massively parallel RPU architecture can achieve acceleration factors of 30,000 compared to state-of-the-art microprocessors ... problems that currently require days of training on a datacenter-size cluster with thousands of machines can be addressed within hours on a single RPU accelerator, " according to the paper.

The scientists believe its possible to build such chips using regular CMOS technology, but for now RPU's are still in the research phase. Furthermore, the technology behind it, like resistive RAM, has yet to be commercialized. However, building chips with fast local memory is a logical idea that could dramatically speed up AI tasks like image processing, language mastery and large-scale data analysis -- you know, all the things experts say we should be worried about.