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 combine both operations. Doing so can reduce the energy and time needed for conventional microchips to shuttle data between processors and memory. Each human neuron may have thousands of synapses connecting them with other cells; one kind of neuron found in the cerebellum, the Purkinje cell, may have as many as 100,000 synapses. This extraordinary level of connectivity lets each human neuron “combine different pieces of information, compare them, and respond depending on the context,” Yan says. In contrast, most conventional neuromorphic devices are essentially “one artificial synapse,” Yan says. Building an artificial neuron with as many synapses as a human neuron would require wiring many separate devices together. “This increases wiring, energy cost, and hardware complexity,” Yan says. Using Quantum-Like Tricks Enables Parallel Computing Recently, scientists have developed acoustic devices in which sound waves can encode multiple values in its waves phase. These phase bits, or phi-bits, can in turn support quantum-like logic gates and parallel