Adnan Mehonic
Professor of Nanoelectronics and Electronic Materials
Researches memristive technologies for neuromorphic computing and energy-efficient AI hardware.
Adnan Mehonic's lab at UCL works on accelerators, chips & architecture and efficient training & inference. 21 papers since 2023, 1 at top venues, mostly Nature Communications.
At a glance
- papers, 2023–2026
- 5/ year
- at T1 venues
- 5%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 1 papers2026 · T1 0 · collecting
- 6 papers2025 · T1 0
- 7 papers2024 · T1 1
- 7 papers2023 · T1 0
T1 venuesOtherVenues: Nature Communications
Research focuspaper titles that use each topic's words
- neuromorphic4 papers
- Accelerators, chips & architecture3 papers
- Efficient training & inference2 papers
- neuromorphic computing2 papers
- energy-efficient1 paper
Paper list
From the lab homepage.
20261 paper
- Nonvolatile Memory and Selector DevicesIntroduction
20256 papers
- APL Machine LearningAPL Machine Learning reviewer acknowledgment for 2024
- APL MACHINE LEARNINGEditorial: The intersection of machine learning and physical sciences: Insights from the 2024 Nobel Prizes
- APL MACHINE LEARNINGHard way or hardware? Taking the heat out of AI
- APL MACHINE LEARNINGNonideality-aware training makes memristive networks more robust to adversarial attacks
- SEAISpiking Neural Network with Silicon Oxide Memristive Devices in the Subthreshold Regime (opens in a new tab)
- ADVANCED FUNCTIONAL MATERIALSUltra-Fast Non-Volatile Resistive Switching Devices with Over 512 Distinct and Stable Levels for Memory and Neuromorphic Computing
20247 papers
- MetroXRAINEA Compact SPICE Model for Current Transients within the Subthreshold Regime of Memristors. (opens in a new tab)
- Nanocrystals in Nonvolatile Memory Second EditionEmerging nonvolatile memories for machine learning
- Nature Communications (T1)Hardware implementation of memristor-based artificial neural networks (opens in a new tab)
- APL MaterialsRoadmap to neuromorphic computing with emerging technologies (opens in a new tab)
- NANOSpiking Neural Networks with Nonidealities from Memristive Silicon Oxide Devices (opens in a new tab)
- Advanced MaterialsThe Role of Hydrogen in ReRAM (opens in a new tab)
- APL MACHINE LEARNINGWebinar Recap: Fostering a New Data Culture with <i>APL Machine Learning</i>
20237 papers
- Nature ElectronicsA full spectrum of computing-in-memory technologies (opens in a new tab)
- Advanced Memory TechnologyAlgorithmic Optimisation for Memristive Deep Learning Accelerators
- APL Machine LearningBrains and bytes: Trends in neuromorphic technology (opens in a new tab)
- Neuromorphic Computing and EngineeringEditorial: Focus issue on energy-efficient neuromorphic devices, systems and algorithms (opens in a new tab)
- APL MACHINE LEARNINGEditorial: Welcome to APL Machine Learning
- Science AdvancesThin-film design of amorphous hafnium oxide nanocomposites enabling strong interfacial resistive switching uniformity (opens in a new tab)
- IEEE Transactions on NanotechnologyUnipolar potentiation and depression in memristive devices utilising the subthreshold regime (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to UCLFall 2027
Not announced yet
Computer Science MPhil/PhD
- Round
- Main
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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Data
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- The member list could not be found on the lab site.
- The alumni list could not be found on the lab site.
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- The lab homepage
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- Alumni
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