Sebastian, Abu

Memory devices and applications for in-memory computing

Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R., & Eleftheriou, E. (2020) · Nature Nanotechnology

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 2,289

Neuromorphic computing with multi-memristive synapses

Boybat, I., Le Gallo, M., Nandakumar, S. R., Moraitis, T., Parnell, T., Tuma, T., Rajendran, B., Leblebici, Y., Sebastian, A., & Eleftheriou, E. (2018) · Nature Communications

Advanced Memory and Neural ComputingNeural Networks and Reservoir Computing

Cited by 899

Hardware implementation of memristor-based artificial neural networks

Aguirre, F., Sebastian, A., Le Gallo, M., Song, W., Wang, T., Yang, J. J., Lu, W., Chang, M.-F., Ielmini, D., Yang, Y., et al. (2024) · Nature Communications

Advanced Memory and Neural ComputingNeural Networks and Applications

Cited by 564

Accurate deep neural network inference using computational phase-change memory

Joshi, V., Le Gallo, M., Haefeli, S., Boybat, I., Nandakumar, S. R., Piveteau, C., Dazzi, M., Rajendran, B., Sebastian, A., & Eleftheriou, E. (2020) · Nature Communications

Advanced Memory and Neural ComputingAdvanced Neural Network Applications

Cited by 490

Mixed-precision in-memory computing

Le Gallo, M., Sebastian, A., Mathis, R., Manica, M., Giefers, H., Tuma, T., Bekas, C., Curioni, A., & Eleftheriou, E. (2018) · Nature Electronics

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 485

An overview of phase-change memory device physics

Le Gallo, M., & Sebastian, A. (2020) · Journal of Physics D: Applied Physics

Phase-change materials and chalcogenidesAdvanced Memory and Neural Computing

Cited by 460

Recent Progress in Phase-ChangeMemory Technology

Burr, G. W., BrightSky, M. J., Sebastian, A., Cheng, H.-Y., Wu, J.-Y., Kim, S., Sosa, N. E., Papandreou, N., Lung, H.-L., Pozidis, H., et al. (2016) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems

Advanced Memory and Neural ComputingPhase-change materials and chalcogenides

Cited by 393

A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference

Le Gallo, M., Khaddam-Aljameh, R., Stanisavljevic, M., Vasilopoulos, A., Kersting, B., Dazzi, M., Karunaratne, G., Brändli, M., Singh, A., Müller, S. M., et al. (2023) · Nature Electronics

Advanced Memory and Neural ComputingLow-power high-performance VLSI design

Cited by 310

In-memory hyperdimensional computing

Karunaratne, G., Le Gallo, M., Cherubini, G., Benini, L., Rahimi, A., & Sebastian, A. (2020) · Nature Electronics

Advanced Memory and Neural ComputingNeural Networks and Reservoir Computing

Cited by 293

Temporal correlation detection using computational phase-change memory

Sebastian, A., Tuma, T., Papandreou, N., Le Gallo, M., Kull, L., Parnell, T., & Eleftheriou, E. (2017) · Nature Communications

Advanced Memory and Neural ComputingPhase-change materials and chalcogenides

Cited by 241

Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators

Rasch, M. J., Mackin, C., Le Gallo, M., Chen, A., Fasoli, A., Odermatt, F., Li, N., Nandakumar, S. R., Narayanan, P., Tsai, H., et al. (2023) · Nature Communications

Advanced Memory and Neural ComputingAdvanced Neural Network Applications

Cited by 213

Constrained Few-shot Class-incremental Learning

Hersche, M., Karunaratne, G., Cherubini, G., Benini, L., Sebastian, A., & Rahimi, A. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 167

A Flexible and Fast PyTorch Toolkit for Simulating Training and Inference on Analog Crossbar Arrays

Rasch, M. J., Moreda, D., Gokmen, T., Le Gallo, M., Carta, F., Goldberg, C., El Maghraoui, K., Sebastian, A., & Narayanan, V. (2021) · 2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS)

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 160

HERMES-Core—A 1.59-TOPS/mm 2 PCM on 14-nm CMOS In-Memory Compute Core Using 300-ps/LSB Linearized CCO-Based ADCs

Khaddam-Aljameh, R., Stanisavljevic, M., Fornt Mas, J., Karunaratne, G., Brändli, M., Liu, F., Singh, A., Müller, S. M., Egger, U., Petropoulos, A., et al. (2022) · IEEE Journal of Solid-State Circuits

Advanced Memory and Neural ComputingLow-power high-performance VLSI design

Cited by 144

HERMES Core – A 14nm CMOS and PCM-based In-Memory Compute Core using an array of 300ps/LSB Linearized CCO-based ADCs and local digital processing

Khaddam-Aljameh, R., Stanisavljevic, M., Fornt Mas, J., Karunaratne, G., Braendli, M., Liu, F., Singh, A., Müller, S. M., Egger, U., Petropoulos, A., et al. (2021) · 2021 Symposium on VLSI Circuits

Advanced Memory and Neural ComputingLow-power high-performance VLSI design

Cited by 124

Programming algorithms for multilevel phase-change memory

Papandreou, N., Pozidis, H., Pantazi, A., Sebastian, A., Breitwisch, M., Lam, C., & Eleftheriou, E. (2011) · 2011 IEEE International Symposium of Circuits and Systems (ISCAS)

Phase-change materials and chalcogenidesAdvanced Memory and Neural Computing

Cited by 124

Mixed-Precision Deep Learning Based on Computational Memory

Nandakumar, S. R., Le Gallo, M., Piveteau, C., Joshi, V., Mariani, G., Boybat, I., Karunaratne, G., Khaddam-Aljameh, R., Egger, U., Petropoulos, A., et al. (2020) · Frontiers in Neuroscience

Advanced Memory and Neural ComputingAdvanced Neural Network Applications

Cited by 120

Compressed Sensing With Approximate Message Passing Using In-Memory Computing

Le Gallo, M., Sebastian, A., Cherubini, G., Giefers, H., & Eleftheriou, E. (2018) · IEEE Transactions on Electron Devices

Sparse and Compressive Sensing TechniquesAdvanced Memory and Neural Computing

Cited by 99

A collective relaxation model for resistance drift in phase change memory cells

Sebastian, A., Krebs, D., Le Gallo, M., Pozidis, H., & Eleftheriou, E. (2015) · 2015 IEEE International Reliability Physics Symposium

Phase-change materials and chalcogenidesAdvanced Memory and Neural Computing

Cited by 36

Inherent stochasticity in phase-change memory devices

Le Gallo, M., Tuma, T., Zipoli, F., Sebastian, A., & Eleftheriou, E. (2016) · 2016 46th European Solid-State Device Research Conference (ESSDERC)

Advanced Memory and Neural ComputingPhase-change materials and chalcogenides

Cited by 35

Phase-Change Memory Models for Deep Learning Training and Inference

Nandakumar, S. R., Boybat, I., Joshi, V., Piveteau, C., Le Gallo, M., Rajendran, B., Sebastian, A., & Eleftheriou, E. (2019) · 2019 26th IEEE International Conference on Electronics, Circuits and Systems (ICECS)

Advanced Memory and Neural ComputingPhase-change materials and chalcogenides

Cited by 34

Compressed sensing recovery using computational memory

Le Gallo, M., Sebastian, A., Cherubini, G., Giefers, H., & Eleftheriou, E. (2017) · 2017 IEEE International Electron Devices Meeting (IEDM)

Sparse and Compressive Sensing TechniquesAdvanced Memory and Neural Computing

Cited by 28

Mushroom-Type phase change memory with projection liner: An array-level demonstration of conductance drift and noise mitigation

Bruce, R. L., Sarwat, S. G., Boybat, I., Cheng, C.-W., Kim, W., Nandakumar, S. R., Mackin, C., Philip, T., Liu, Z., Brew, K., et al. (2021) · 2021 IEEE International Reliability Physics Symposium (IRPS)

Advanced Memory and Neural ComputingPhase-change materials and chalcogenides

Cited by 23

Stochastic weight updates in phase-change memory-based synapses and their influence on artificial neural networks

Boybat, I., Le Gallo, M., Moraitis, T., Leblebici, Y., Sebastian, A., & Eleftheriou, E. (2017) · 2017 13th Conference on Ph.D. Research in Microelectronics and Electronics (PRIME)

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 20