The broader impact/commercial potential of this I-Corps project is the development of extremely low-power systems for applying machine learning to audio signals. This enables the continuous monitoring complex environments without the need for large power supplies. The initial thrust is in agriculture where monitoring livestock may result in healthier animals and lower antibiotic usage. This may result in improved animal welfare and lower production costs for animal protein. In addition, it may help address the challenge of humans developing antibiotic resistance acquired through meat products consumer. The size-weight-and-power (SWAP) advantages may make the technology attractive to a broad range of other fields including environmental monitoring, manufacturing, security, and new portable medical devices. These innovations could result in enhancing human health and safety.
This I-Corps project is based on the development of methods of adapting traditional artificial neural networks (ANNs) to work reliably even when implemented using approximate computing analog technology. Using these methods in the design of on-chip analog ANNs makes it possible to perform audio event classification in ultra-low-power hardware. In addition, practical machine learning and audio signal processing algorithms are used to efficiently characterize environmental audio using developed proprietary sparse-matrix-encoded dictionary algorithms. These algorithms run on low power edge computation hardware that capture and analyze an audio environment locally. These algorithms also allow robust implementations of ANN for further acoustic classification. When combined with other solutions involving programmable analog circuits, the result replaces power-hungry digital circuitry with more efficient analog processing.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
