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Topological acoustic synapse for high-dimensional neuromorphic computing
Jinli Chen1,2, Akinsanmi S Ige1,2, Keith Runge1,2
1Department of Materials Science and Engineering, University of Arizona, Tucson, AZ 85721, USA.
Science Advances
|June 12, 2026
Summary
Researchers developed a novel topological acoustic synapse (TAS) for efficient neuromorphic computing. This acoustic-wave device offers a scalable paradigm for high-density computation, overcoming limitations of current electronic systems.
Area of Science:
- Neuromorphic Engineering
- Acoustic Computing
- Artificial Intelligence Hardware
Background:
- The human brain performs complex computations efficiently, a feat current neuromorphic computing struggles to replicate due to hardware limitations.
- Existing electronic neuromorphic devices face bottlenecks in bandwidth, energy consumption, wiring, footprint, and reliability, hindering scalability.
Purpose of the Study:
- To introduce a novel neuromorphic device, the topological acoustic synapse (TAS), that overcomes the limitations of current electronic systems.
- To demonstrate the potential of acoustic-wave devices for high-dimensional, energy-efficient neuromorphic computing.
Main Methods:
- Developed a topological acoustic synapse (TAS) utilizing acoustic waves to map information in multivariate state spaces.
- Leveraged nonlinear interactions within the TAS to emulate biorealistic neuromorphic functionalities like synaptic plasticity and neuromodulation.
- Implemented hybrid analog-digital control for reconfigurable computing.
Main Results:
- A single TAS generates and manipulates numerous independent, parallel computing channels.
- The TAS demonstrated superior performance in classification tasks, converging 20% faster with 60% fewer parameters compared to state-of-the-art electrical devices.
- The acoustic synapse achieved at least an order of magnitude reduction in power consumption.
Conclusions:
- The topological acoustic synapse (TAS) represents the first acoustic synapse capable of parallel high-dimensional computing.
- This work presents a scalable paradigm for neuromorphic hardware with high computational density and significantly improved energy efficiency.
- Acoustic-wave devices offer a promising alternative for next-generation neuromorphic computing.
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