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Technology Roadmap of Bioinspired Computing Hardware.

Shuang Wang1,2,3, Zhiyuan Li1,2,3, Mengjiao Pei1,2,3

  • 1Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon, Hong Kong 999077, China.

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Summary
This summary is machine-generated.

Bioinspired computing (BIC) offers a solution to artificial intelligence (AI) hardware limitations by mimicking biological systems. This roadmap details BIC

Keywords:
bioinspired architecturebioinspired computingin-memory computingin-sensor computingneuromorphic chipsneuronal deviceretinomorphic devicesynaptic device

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Area of Science:

  • Cross-disciplinary research at the intersection of materials science, device physics, neuroscience, computer science, mathematics, and information science.

Background:

  • Rapid artificial intelligence (AI) growth faces hardware bottlenecks in computation throughput and energy efficiency.
  • Bioinspired computing (BIC) leverages biological system advantages like parallelism, adaptability, and robustness as a potential alternative.

Purpose of the Study:

  • To provide a comprehensive roadmap for the development of Bioinspired Computing (BIC) hardware.
  • To analyze frontier issues, latest progress, and critical challenges in BIC hardware development.

Main Methods:

  • Categorization of challenges into hardware foundations, architectures, and prototype realizations.
  • Highlighting biological features inspiring BIC hardware design through device physics.
  • Describing signaling rules and structural organizations in BIC architectures for computational prototypes.

Main Results:

  • Discussion of performance metrics and engineering challenges for BIC hardware.
  • Presentation of electronic and photonic BIC chip prototypes.
  • A technological roadmap outlining opportunities for expanding BIC hardware functionality.

Conclusions:

  • Interdisciplinary convergence is crucial for advancing BIC hardware.
  • Coordinated advances in devices, architectures, and system demonstrations can accelerate the shift to high-efficiency AI hardware.