ArticleNanotechnology2021
Roadmap on emerging hardware and technology for machine learning.
Article in Nanotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed.
- Progress in the research of artificial intelligence in andrology: a narrative review.Translational andrology and urology · 2026Review
- Volatile ZrOAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Low-Power Ionically Tunable Bilayer MoSNano letters · 2025Article
- Integrated lithium niobate photonic computing circuit based on efficient and high-speed electro-optic conversion.Nature communications · 2025Article
- Perovskite Neuromorphic Engine for Transformer Architectures.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Article
- In-situ training in programmable photonic frequency circuits.Nanophotonics (Berlin, Germany) · 2025Article
- Sub-milliwatt threshold power and tunable-bias all-optical nonlinear activation function using vanadium dioxide for wavelength-division multiplexing photonic neural networks.Scientific reports · 2025Article
- Quantum-limited stochastic optical neural networks operating at a few quanta per activation.Nature communications · 2025Article
- Challenges and opportunities in engineering next-generation 3D microelectronic devices: improved performance and higher integration density.Nanoscale advances · 2024Review
- Convergence of Nanotechnology and Machine Learning: The State of the Art, Challenges, and Perspectives.International journal of molecular sciences · 2024Review
- Optimisation Challenge for a Superconducting Adiabatic Neural Network That Implements XOR and OR Boolean Functions.Nanomaterials (Basel, Switzerland) · 2024Article
- Diffractive optical computing in free space.Nature communications · 2024Review
- Raman spectra and defect chemical characteristics of Sr(Ti,Fe)OJournal of materials chemistry. A · 2023Article
- Enhanced electrical and magnetic properties of (Co, Yb) co-doped ZnO memristor for neuromorphic computing.RSC advances · 2023Article
- Quantum-noise-limited optical neural networks operating at a few quanta per activation.Research square · 2023Article
- Bio-Inspired Design of Superconducting Spiking Neuron and Synapse.Nanomaterials (Basel, Switzerland) · 2023Article
- Comparison of the Usability of Apple M2 and M1 Processors for Various Machine Learning Tasks.Sensors (Basel, Switzerland) · 2023Article
- Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders-A Scoping Review.Sensors (Basel, Switzerland) · 2023Article
- Photonic online learning: a perspective.Nanophotonics (Berlin, Germany) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
45 authors.
Funding
Abstract
Recent progress in artificial intelligence is largely attributed to the rapid development of machine learning, especially in the algorithm and neural network models. However, it is the performance of the hardware, in particular the energy efficiency of a computing system that sets the fundamental limit of the capability of machine learning. Data-centric computing requires a revolution in hardware systems, since traditional digital computers based on transistors and the von Neumann architecture were not purposely designed for neuromorphic computing. A hardware platform based on emerging devices and new architecture is the hope for future computing with dramatically improved throughput and energy efficiency. Building such a system, nevertheless, faces a number of challenges, ranging from materials selection, device optimization, circuit fabrication and system integration, to name a few. The aim of this Roadmap is to present a snapshot of emerging hardware technologies that are potentially beneficial for machine learning, providing the Nanotechnology readers with a perspective of challenges and opportunities in this burgeoning field.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.