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TinyNS: Platform-Aware Neurosymbolic Auto Tiny Machine Learning.

Swapnil Sayan Saha1, Sandeep Singh Sandha2, Mohit Aggarwal3

  • 1University of California - Los Angeles, Los Angeles, CA, USA.

ACM Transactions on Embedded Computing Systems : TECS
|June 27, 2024
PubMed
Summary
This summary is machine-generated.

TinyNS is a new framework for creating interpretable AI systems on resource-constrained devices. It optimizes both symbolic reasoning and machine learning models for edge applications, outperforming traditional methods.

Keywords:
AutoMLBayesianTinyMLneural architecture searchneurosymbolicplatform-aware

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Embedded Systems

Background:

  • Deploying interpretable AI with symbolic reasoning on edge devices is challenging due to resource constraints.
  • Existing methods struggle to balance symbolic integrity with machine learning performance under strict hardware limitations.

Purpose of the Study:

  • Introduce TinyNS, a platform-aware neurosymbolic architecture search framework.
  • Enable joint optimization of symbolic and neural operators for edge AI applications.
  • Facilitate the creation of deployable neurosymbolic models on microcontrollers.

Main Methods:

  • Developed TinyNS, a framework for automatic microcontroller code generation for neurosymbolic models.
  • Utilized a gradient-free, black-box Bayesian optimizer for efficient search over complex spaces.
  • Integrated hardware-aware optimization to ensure real-world deployability.

Main Results:

  • TinyNS successfully deployed microcontroller-class neurosymbolic models across several case studies.
  • The framework automatically generates code for five types of neurosymbolic models.
  • Optimized neurosymbolic models demonstrated superior performance compared to purely neural or symbolic approaches.

Conclusions:

  • TinyNS effectively combines symbolic reasoning and machine learning for edge AI.
  • The framework guarantees execution on real hardware, overcoming deployment challenges.
  • TinyNS represents a significant advancement in developing robust and interpretable AI for resource-constrained environments.