Sound Waves Give Neuromorphic Chips a Brain-Simulating Edge
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.
Key Takeaways
- "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.
- 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.
- Whereas conventional bits only symbolize one of two digits, 0 or 1, and require a separate physical component for each bit, phi-bits each represent multiple variables and coexist within one space.
To be clear, however, operations on phi-bits are not quantum computations , only classical analogues of quantum computer systems.
- The new device the scientists developed consists of three aluminum rods, each roughly 60 centimeters long and 1.25 centimeters wide, and connected by epoxy glue.
The researchers used a thin layer of honey to attach ultrasonic transmitters and sensors to the ends of the rods.
- This property, called synaptic plasticity , meant the researchers could train their acoustic synapse to perform a range of tasks.
"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.
For more details please read the original article at IEEE Spectrum AI.
Continue Learning
Comments
Comments appear only after moderation. Your email identifies your submission to the moderator and is never displayed here.
No approved comments yet.