A Neuromorphic Chip That Makes Music

The prototype chip learns a style of music, then composes its own tunes

3 min read
This prototype chip learns a style of music, then composes its own tunes.
Photo: IMEC

A chip made by researchers at IMEC in Belgium uses brain-inspired circuits to compose melodies. The prototype neuromorphic chip learns the rules of musical composition by detecting patterns in the songs it’s exposed to. It then creates its own song in the same style. It’s an early demo from a  project to develop low-power, general purpose learning accelerators that could help tailor medical sensors to their wearers and enable personal electronics to learn their users’ patterns of behavior.

Today’s connected devices don’t have much smarts on board—instead they send data into the cloud for analysis by remote servers, where energy use and cooling costs are not at a premium, says Praveen Raghavan, who leads technology development for neuromorphic computation at IMEC. The IMEC team wants to change this. “The whole objective is to make artificial intelligence more compact, and bring it closer to the user,” he says. That means making compact, low-power dedicated learning chips. “We want to be as cost effective as possible,” he says.

The chip tune released by IMEC definitely sounds derivative. The composer has made a few odd note selections, but nothing that could be called avant garde—it’s in the mold of a certain strain of western classical music. Indeed, the 30-second tune evokes the simple melodies beginning musicians practice over and over. Two bars are very close to a riff on the chromatic scale; in the last bar, it resolves on a note that feels pleasing and expected.

For the chip to generate this tune, it was sequentially loaded with songs in the same time signature (which specifies how many beats are in each bar of music) and style. If exposed to a broad range of rhythms and styles, it wouldn’t have been able to discern the patterns at work. Raghavan says the prototype was taught using old Belgian and French flute minuets. Based on this, the chip learned the rules at play and then applied them. As the inputs are switched to a different time signature, it will start to learn that one, says Raghavan.

The prototype neuromorphic chip uses a one megabit bank of resistive RAM built on top of a processor. The memory cells are filaments of an oxide material whose electrical resistance varies along a continuum of states in response to different applied voltages. When the chip “hears” two particular notes one after the other in multiple training songs, it detects a pattern. The more often the notes occur together, the stronger the association between them, and the stronger the connection between the two memory cells that store them. In turn, it becomes more likely the chip will put those two notes in sequence when it switches from learning to composing mode. This is analogous to the way connections between different neurons in the brain strengthen or weaken as we learn. The system is hierarchical, and can spot and learn broader patterns, too. “It starts to see patterns in the music in the short term and long term,” says Raghavan.

Raghavan would not say how much power the prototype used—he says they overdesigned it so that it could take on a variety of tasks, building large memory cells that are responsive to a range of voltages. The music task doesn’t make use of the entire memory bank, either—but future, more complex tasks likely will. Now that they’ve studied the hardware’s capabilities, he says, they’ll optimize the design. Raghavan says it’s already flexible. The group has also trained the system with text, teaching it to autocomplete sentences.

Raghavan says these systems could be used as learning accelerators in personal electronics, or combined with other hardware designed to run neural networks. One application he has in mind is a heart-rate monitor for a smart watch that would be able to adjust to the user’s style to get more accurate readings. “Some people wear it tight, some loose, some higher or lower on the arm,” he says. All this can affect the accuracy of the measurements.

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Will AI Steal Submarines’ Stealth?

Better detection will make the oceans transparent—and perhaps doom mutually assured destruction

11 min read
A photo of a submarine in the water under a partly cloudy sky.

The Virginia-class fast attack submarine USS Virginia cruises through the Mediterranean in 2010. Back then, it could effectively disappear just by diving.

U.S. Navy

Submarines are valued primarily for their ability to hide. The assurance that submarines would likely survive the first missile strike in a nuclear war and thus be able to respond by launching missiles in a second strike is key to the strategy of deterrence known as mutually assured destruction. Any new technology that might render the oceans effectively transparent, making it trivial to spot lurking submarines, could thus undermine the peace of the world. For nearly a century, naval engineers have striven to develop ever-faster, ever-quieter submarines. But they have worked just as hard at advancing a wide array of radar, sonar, and other technologies designed to detect, target, and eliminate enemy submarines.

The balance seemed to turn with the emergence of nuclear-powered submarines in the early 1960s. In a 2015 study for the Center for Strategic and Budgetary Assessment, Bryan Clark, a naval specialist now at the Hudson Institute, noted that the ability of these boats to remain submerged for long periods of time made them “nearly impossible to find with radar and active sonar.” But even these stealthy submarines produce subtle, very-low-frequency noises that can be picked up from far away by networks of acoustic hydrophone arrays mounted to the seafloor.

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