I. Boybat

发表

HYBRID CMOS-MEMRISTIVE NEUROMORPHIC SYSTEMS MODELING AND DESIGN

I. Boybat, 2015 .

A phase-change memory model for neuromorphic computing

E. Eleftheriou, B. Rajendran, A. Sebastian, 2018, Journal of Applied Physics.

Supervised learning in spiking neural networks with MLC PCM synapses

B. Rajendran, S. R. Nandakumar, A. Sebastian, 2017, 2017 75th Annual Device Research Conference (DRC).

ALPINE: Analog In-Memory Acceleration with Tight Processor Integration for Deep Learning

David Atienza Alonso, A. Sebastian, I. Boybat, 2022, 2205.10042.

Graphene-Based Wireless Agile Interconnects for Massive Heterogeneous Multi-Chip Processors

Elana Pereira de Santana, Francesco Conti, H. Taghvaee, 2020, IEEE wireless communications.

A Heterogeneous In-Memory Computing Cluster for Flexible End-to-End Inference of Real-World Deep Neural Networks pdf

Luca Benini, Davide Rossi, Francesco Conti, 2022, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

ML-HW Co-Design of Noise-Robust TinyML Models and Always-On Analog Compute-in-Memory Edge Accelerator

Fernando García Redondo, Xavier Timoneda Comas, P. Whatmough, 2022, IEEE Micro.

A Heterogeneous and Programmable Compute-In-Memory Accelerator Architecture for Analog-AI Using Dense 2-D Mesh

P. Narayanan, G. Burr, Stanisław Woźniak, 2023, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

End-to-End DNN Inference on a Massively Parallel Analog In Memory Computing Architecture

Francesco Conti, L. Benini, D. Rossi, 2022, ArXiv.

Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics

J. Kwisthout, H. Riel, A. Sebastian, 2022, ArXiv.

Scale up your In-Memory Accelerator: Leveraging Wireless-on-Chip Communication for AIMC-based CNN Inference

Francesco Conti, L. Benini, S. Abadal, 2022, 2022 IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS).

ALPINE: Analog In-Memory Acceleration With Tight Processor Integration for Deep Learning

David Atienza Alonso, A. Sebastian, I. Boybat, 2022, IEEE Transactions on Computers.

Neuromorphic computing using non-volatile memory

Pritish Narayanan, Geoffrey W. Burr, Kumar Virwani, 2017 .

Equivalent-accuracy accelerated neural-network training using analogue memory

Nathan C. P. Farinha, P. Narayanan, G. Burr, 2018, Nature.

Large-scale neural networks implemented with non-volatile memory as the synaptic weight element: Comparative performance analysis (accuracy, speed, and power)

Y. Leblebici, R. M. Shelby, C. di Nolfo, 2015, 2015 IEEE International Electron Devices Meeting (IEDM).

Neuromorphic computing with multi-memristive synapses

Yusuf Leblebici, Thomas P. Parnell, Evangelos Eleftheriou, 2017, Nature Communications.

Accurate deep neural network inference using computational phase-change memory

Evangelos Eleftheriou, S. R. Nandakumar, Irem Boybat, 2019, Nature Communications.

Mixed-precision architecture based on computational memory for training deep neural networks

Evangelos Eleftheriou, S. R. Nandakumar, Irem Boybat, 2018, 2018 IEEE International Symposium on Circuits and Systems (ISCAS).

Fatiguing STDP: Learning from spike-timing codes in the presence of rate codes

Evangelos Eleftheriou, Irem Boybat, Abu Sebastian, 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

Experimental Demonstration of Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses

Evangelos Eleftheriou, Bipin Rajendran, S. R. Nandakumar, 2019, Scientific Reports.

Equivalent-accuracy accelerated neural-network training using analogue memory

Pritish Narayanan, Stefano Ambrogio, Irem Boybat, 2018, Nature.

Signal and noise extraction from analog memory elements for neuromorphic computing

V. Narayanan, A. Sebastian, T. Ando, 2018, Nature Communications.

Mixed-Precision Deep Learning Based on Computational Memory

E. Eleftheriou, B. Rajendran, A. Sebastian, 2020, Frontiers in Neuroscience.

Multi-ReRAM Synapses for Artificial Neural Network Training

Yusuf Leblebici, Evangelos Eleftheriou, Irem Boybat, 2019, 2019 IEEE International Symposium on Circuits and Systems (ISCAS).

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

Yusuf Leblebici, Evangelos Eleftheriou, Irem Boybat, 2017, 2017 13th Conference on Ph.D. Research in Microelectronics and Electronics (PRIME).

Experimental Demonstration and Tolerancing of a Large-Scale Neural Network (165 000 Synapses) Using Phase-Change Memory as the Synaptic Weight Element

Pritish Narayanan, Kumar Virwani, Hyunsang Hwang, 2014, IEEE Transactions on Electron Devices.

Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics

J. Kwisthout, H. Riel, A. Sebastian, 2022, APL Machine Learning.

Phase-Change Memory Models for Deep Learning Training and Inference

Irem Boybat, Manuel Le Gallo, Abu Sebastian, 2019, 2019 26th IEEE International Conference on Electronics, Circuits and Systems (ICECS).

AnalogNets: ML-HW Co-Design of Noise-robust TinyML Models and Always-On Analog Compute-in-Memory Accelerator pdf

S. R. Nandakumar, Irem Boybat, Abu Sebastian, 2021, ArXiv.

Impact of Phase-Change Memory Drift on Energy Efficiency and Accuracy of Analog Compute-in-Memory Deep Learning Inference (Invited)

M. Rasch, P. Narayanan, G. Burr, 2023, 2023 IEEE International Reliability Physics Symposium (IRPS).

Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems.

J. Yang, Shinhyun Choi, I. Valov, 2023, ACS nano.

Computational memory-based inference and training of deep neural networks

B. Rajendran, S. R. Nandakumar, A. Petropoulos, 2019, 2019 Symposium on VLSI Technology.

AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computing

Kaoutar El Maghraoui, M. Rasch, A. Sebastian, 2023, 2023 IEEE International Conference on Edge Computing and Communications (EDGE).

Temperature sensitivity of analog in-memory computing using phase-change memory

A. Sebastian, M. BrightSky, R. Bruce, 2021, 2021 IEEE International Electron Devices Meeting (IEDM).

Impact of conductance drift on multi-PCM synaptic architectures

Yusuf Leblebici, Evangelos Eleftheriou, S. R. Nandakumar, 2018, 2018 Non-Volatile Memory Technology Symposium (NVMTS).

Deep learning acceleration based on in-memory computing

Nikolas Ioannou, Evangelos Eleftheriou, Pier Andrea Francese, 2019, IBM J. Res. Dev..

Large-scale neural networks implemented with Non-Volatile Memory as the synaptic weight element: Impact of conductance response

Yusuf Leblebici, Pritish Narayanan, Hyunsang Hwang, 2016, 2016 46th European Solid-State Device Research Conference (ESSDERC).

Experimental demonstration and tolerancing of a large-scale neural network (165,000 synapses), using phase-change memory as the synaptic weight element

G. W. Burr, R. M. Shelby, C. di Nolfo, 2015, 2014 IEEE International Electron Devices Meeting.

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

S. Ambrogio, A. Sebastian, T. Yamashita, 2021, 2021 IEEE International Reliability Physics Symposium (IRPS).

Precision of bit slicing with in-memory computing based on analog phase-change memory crossbars

A. Sebastian, I. Boybat, S. Nandakumar, 2022, Neuromorph. Comput. Eng..

Accurate Emulation of Memristive Crossbar Arrays for In-Memory Computing

Theodore Antonakopoulos, Evangelos Eleftheriou, Irem Boybat, 2020, 2020 IEEE International Symposium on Circuits and Systems (ISCAS).

Mixed-Precision Deep Learning Based on Computational Memory

Theodore Antonakopoulos, Evangelos Eleftheriou, Bipin Rajendran, 2020, Frontiers in Neuroscience.

Signal and noise extraction from analog memory elements for neuromorphic computing

N Gong, T Idé, S Kim, 2018, Nature Communications.

Mixed-precision training of deep neural networks using computational memory pdf

Evangelos Eleftheriou, S. R. Nandakumar, Irem Boybat, 2017, ArXiv.

Graphene-Based Wireless Agile Interconnects for Massive Heterogeneous Multi-Chip Processors

Elana Pereira de Santana, Francesco Conti, S. Abadal, 2020, IEEE Wireless Communications.

Architecting more than Moore: wireless plasticity for massive heterogeneous computer architectures (WiPLASH)

Elana Pereira de Santana, Francesco Conti, S. Abadal, 2021, CF.

End-to-end 100-TOPS/W Inference With Analog In-Memory Computing: Are We There Yet?

Luca Benini, Davide Rossi, Francesco Conti, 2021, 2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS).