Irem Boybat

发表

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

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

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).

ESSOP: Efficient and Scalable Stochastic Outer Product Architecture for Deep Learning pdf

Evangelos Eleftheriou, Bipin Rajendran, Irem Boybat, 2020, ISCAS.

Computational memory-based inference and training of deep neural networks

Evangelos Eleftheriou, Bipin Rajendran, Irem Boybat, 2019, 2019 Symposium on VLSI Circuits.

An efficient synaptic architecture for artificial neural networks

Yusuf Leblebici, Evangelos Eleftheriou, S. R. Nandakumar, 2017, 2017 17th Non-Volatile Memory Technology Symposium (NVMTS).

Equivalent-accuracy accelerated neural-network training using analogue memory

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

Multilayer Perceptron Algorithm: Impact of Nonideal Conductance and Area-Efficient Peripheral Circuits

Pritish Narayanan, Geoffrey W. Burr, Yusuf Leblebici, 2017 .

Nonvolatile Memory Crossbar Arrays for Non-von Neumann Computing

Pritish Narayanan, Kumar Virwani, Hyunsang Hwang, 2015 .

Neuromorphic computing with multi-memristive synapses

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

Improved Deep Neural Network Hardware-Accelerators Based on Non-Volatile-Memory: The Local Gains Technique

Yusuf Leblebici, Pritish Narayanan, Stefano Ambrogio, 2017, 2017 IEEE International Conference on Rebooting Computing (ICRC).

A hardware-oriented dynamically adaptive disparity estimation algorithm and its real-time hardware

Baris Atakan, Yusuf Leblebici, Alexandre Schmid, 2013, GLSVLSI '13.

Mixed-Precision Deep Learning Based on Computational Memory

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

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.

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).

Non-volatile memory as hardware synapse in neuromorphic computing: A first look at reliability issues

Irem Boybat, Carmelo di Nolfo, Robert M. Shelby, 2015, 2015 IEEE International Reliability Physics Symposium.

Multi-ReRAM Synapses for Artificial Neural Network Training

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

Neuromorphic computing using non-volatile memory

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

Graphene-based Wireless Agile Interconnects for Massive Heterogeneous Multi-chip Processors pdf

David Atienza, Marina Zapater, Irem Boybat, 2020, ArXiv.

Impact of conductance drift on multi-PCM synaptic architectures

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

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

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

PCM for Neuromorphic Applications: Impact of Device Characteristics on Neural Network Performance

Yusuf Leblebici, Pritish Narayanan, Irem Boybat, 2015 .

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).

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).

Accurate deep neural network inference using computational phase-change memory

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

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).

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).

Cognitive Computing with Non-Volatile Memory Elements

Yusuf Leblebici, Geoffrey W. Burr, Irem Boybat, 2015 .

Accurate Weight Mapping in a Multi-Memristive Synaptic Unit

Robert L. Bruce, S. R. Nandakumar, Irem Boybat, 2021, 2021 IEEE International Symposium on Circuits and Systems (ISCAS).

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.

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).

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.

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

David Atienza, Davide Rossi, Marina Zapater, 2021, CF.