Emerging memories enable AI market

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Stanford University, USA, in collaboration with Atascadero, California, USA-based Coughlin Associates, is organizing a workshop on emerging non-volatile memories (NVM) and artificial intelligence (AI), on on August 29, 2019.

The one-day workshop at Stanford University, put on by the Stanford Center for Magnetic Nanotechnology and Coughlin Associates, features invited expert speakers to talk about various emerging NVMs, and how they will enable the next-generation of AI devices in the home, in the factory and in the industry.

Subhasish Mitra, Prof. Electrical Engineering and Computer Science, Stanford University, will talk about RRAM integrated on silicon CMOS for AI applications. A very good friend, Thomas Coughlin, president of Coughlin Associates, will be speaking on how emerging memories enable the AI market.

RRAM, also known as ReRAM (resistive random access memory), is a form of non-volatile storage that operates by changing the resistance of a specially formulated solid dielectric material.

Tom Coughlin

Elaborating on RRAM for AI applications, specifically, RRAM integrated on silicon CMOS for AI app, Tom Coughlin said: “AI inference engines are looking at MRAM, and possibly, RRAM for storing ML weighting functions. This would be either for the edge computing or end-point applications. RRAM, as well as PCM, are being pursued for neuromorphic computing architectures that use memory cell technology for analog computing, similar to the way that neurons work in the brain.

How can we understand ML and its potential in the semicon industry? Coughlin added that AI allows the unlocking of greater value in the data and information that we capture, and thus making better decisions based upon that data. This has a huge value, and semiconductor technologies enabling AI will play a big role.

I also got his views on edge-AI and the rise of the neural accelerators. He added: “A lot of edge work will be done with inference engines using models developed in data centers. Although, there are approaches to allow some continuous learning. I see this as becoming a big enabler of smart devices and applications.”

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