Sound Waves Give Neuromorphic Chips a Brain-Simulating Edge
IEEE Spectrum AI Charles Q. Choi
Researchers developed an acoustic synapse using sound waves and phase bits to create neuromorphic hardware that mimics biological neurons more closely than electronic alternatives. The device achieved 96.7 percent accuracy on iris flower classification using 39 parameters and 20 percent faster than conventional neural networks while consuming one-tenth the power of current electronic neuromorphic hardware. This approach enables more compact and energy-efficient neuromorphic systems capable of mimicking neuromodulation and handling complex pattern recognition tasks with simpler designs.
Why it matters
By mimicking how the brain operates, neuromorphic computing can use dramatically less energy than conventional electronic AI chips. However, even the most sophisticated neuromorphic devices today are still quite simple, using only a small fraction of the number of connections found in human neurons. Now, a new study suggests that by using sound waves, neuromorphic devices can better mimic biological neurons and operate faster and with greater energy efficiency than their electronic counterparts.“This could make future neuromorphic hardware more compact, more parallel, and more efficient for tasks that require combining many features, such as pattern recognition, sensory processing, and data analysis,” says Xiaodong Yan, an assistant professor of materials science and engineering and electrical and computer engineering at the University of Arizona in Tucson.Just as brains use synapses—the links connecting neurons—to help them both compute and store data, neuromorphic devices often combi
