mlsanityData to 7 Oct 2026

Roadmap to Neuromorphic Computing with Emerging Technologies

arXiv 2407.02353 ↗First posted 2 Jul 2024eess.SP cs.AR cs.SYcited by 12

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Abstract (arXiv)

The roadmap is organized into several thematic sections, outlining current computing challenges, discussing the neuromorphic computing approach, analyzing mature and currently utilized technologies, providing an overview of emerging technologies, addressing material challenges, exploring novel computing concepts, and finally examining the maturity level of emerging technologies while determining the next essential steps for their advancement.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jul 2024.

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Citing papers per year: 2024: 1; 2025: 11.

12 papers.

  1. 2025Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Timecited by 4
  2. 2024Neuromorphic Wireless Split Computing with Multi-Level Spikescited by 3
  3. 2025Convergence of energy-based learning in linear resistive networkscited by 2
  4. 2025Unsupervised Particle Tracking with Neuromorphic Computingcited by 1
  5. 2025Neuromorphic Readout for Hadron Calorimeterscited by 1
  6. 2025Energy-convergence trade off for the training of neural networks on bio-inspired hardwarecited by 1
  7. 2025CIMNAS: A Joint Framework for Compute-In-Memory-Aware Neural Architecture Searchcited by 1
  8. 2025Random Spiking Neural Networks are Stable and Spectrally Simplecited by 1
  9. 2025Spiking Neural Networks for SAR Interferometric Phase Unwrapping: A Theoretical Framework for Energy-Efficient Processingcited by 0
  10. 2025On-chip wave chaos for photonic extreme learningcited by 0
  11. 2025Neural networks for neurocomputing circuits: a computational study of tolerance to noise and activation function non-uniformity when machine learning materials propertiescited by 0
  12. 2025Personalized Spiking Neural Networks with Ferroelectric Synapses for EEG Signal Processingcited by 0