# Roadmap to Neuromorphic Computing with Emerging Technologies

arXiv: https://arxiv.org/abs/2407.02353 · HTML: https://mlsanity.com/paper/2407.02353/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2024.

## Built on by (most cited first, 12 of 12)

- [Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time](https://mlsanity.com/paper/2505.18023/) (2025)
- [Neuromorphic Wireless Split Computing with Multi-Level Spikes](https://mlsanity.com/paper/2411.04728/) (2024)
- [Convergence of energy-based learning in linear resistive networks](https://mlsanity.com/paper/2503.00349/) (2025)
- [Unsupervised Particle Tracking with Neuromorphic Computing](https://mlsanity.com/paper/2502.06771/) (2025)
- [Neuromorphic Readout for Hadron Calorimeters](https://mlsanity.com/paper/2502.12693/) (2025)
- [Energy-convergence trade off for the training of neural networks on bio-inspired hardware](https://mlsanity.com/paper/2509.18121/) (2025)
- [CIMNAS: A Joint Framework for Compute-In-Memory-Aware Neural Architecture Search](https://mlsanity.com/paper/2509.25862/) (2025)
- [Random Spiking Neural Networks are Stable and Spectrally Simple](https://mlsanity.com/paper/2511.00904/) (2025)
- [Spiking Neural Networks for SAR Interferometric Phase Unwrapping: A Theoretical Framework for Energy-Efficient Processing](https://mlsanity.com/paper/2506.20782/) (2025)
- [On-chip wave chaos for photonic extreme learning](https://mlsanity.com/paper/2508.19878/) (2025)
- [Neural networks for neurocomputing circuits: a computational study of tolerance to noise and activation function non-uniformity when machine learning materials properties](https://mlsanity.com/paper/2510.17849/) (2025)
- [Personalized Spiking Neural Networks with Ferroelectric Synapses for EEG Signal Processing](https://mlsanity.com/paper/2601.00020/) (2025)

## 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.