Maria Montero

The University of Michigan has developed a computer based on the …

AI on a smartphone? U of M researchers open the door to localized AI processing, even on wearable devices.

Depending on who you ask, AI is already everywhere. From making processes more efficient to causing a stir in disease diagnosis, the presence of AI can be felt across all industries and on the smartphone in your pocket.

But is AI really present on your devices if processing is done in the cloud?

Currently, hardware limitations mean that localized AI – that is, AI that deals with data at the edge rather than the cloud – is rather out of reach.

What the University of Michigan just announced this week is not simply a computer that simply implements memristors; This is a device that heralds advances in localized AI.

Meet “the first programmable memristor computer”.

A Programmable Memristor Computer

“Everyone wants to put an artificial intelligence processor in smartphones, but you don’t want your cell phone battery to drain very quickly,” says Wei Lu, professor of electrical and computer engineering at the University of Michigan (UM). Lu is the lead author of “A Fully Integrated Reprogrammable Memristor System – CMOS for Efficient Multiple Accumulation Operations” published in Nature Electronics.

Lu refers to the incredible amount of battery power it would take an average handheld to maintain AI data processing levels.

As it stands now, AI functions like voice command interpretation require communication with remote cloud-based AI engines. This takes time, and is currently unavoidable because so much AI power in a smartphone would drain the battery very quickly.

The memristor array chip in question, shown here, is connected to the custom computer chip. Photo by Robert Coelius, Michigan Engineering Communications and Marketing.

According to Lu and his team, computer systems based on memristors may be the answer.

What is a memristor?

A memristor can be described as a resistor whose resistance value is determined by the previous voltages and the load to which it has been subjected. If the memristor is not subjected to more voltage or load, the resistance does not change, the way nonvolatile memory does.

This is analogous to the most basic unit in digital logic, the flip-flop. Once the flip-flop output goes to “1” or “0”, it remains at that value. Therefore, the resistive value of the memristor has the same purpose as the memory value “1” or “0” stored by the flip-flop.

To learn more about the basics of memristor / AI, see our previous article on a prototype memristor network inspired by mammalian brains, which builds on some of Professor Lu’s earlier work.

The key to more efficient AI

Machine learning and artificial intelligence algorithms must deal with huge amounts of data to do things like identify objects in photos and videos. The current state of the art relies on separate GPUs (graphics processing units) for that task.

The key to the efficiency of the GPU is that it has large numbers of tiny cores enabled to do all the necessary calculations at once. The CPU, on the other hand, typically has two to eight large cores, and the necessary calculations must wait in line for processing.