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D-Index & Metrics

Electronics and Electrical Engineering

D-Index
42
Citations
11205
World Ranking
4018
National Ranking
210

Overview

Bipin Rajendran is affiliated with King's College London in the United Kingdom. Their research primarily spans the domains of engineering, computer science, and neuroscience, with significant contributions to the fields of electrical and electronic engineering, artificial intelligence, and cognitive as well as cellular and molecular neuroscience.

The scientist's work focuses on advanced topics in memory and neural computing, including ferroelectric and negative capacitance devices. Their research also addresses neural dynamics and brain function, neural networks and reservoir computing, neural networks and applications, CCD and CMOS imaging sensors, and neuroscience and neural engineering.

Frequent publication venues for Rajendran include:

  • arXiv (Cornell University)
  • Frontiers in Neuroscience
  • IEEE Transactions on Machine Learning in Communications and Networking
  • Repository for Publications and Research Data (ETH Zurich)
  • Advanced Intelligent Systems

Rajendran has collaborated extensively with several researchers, notably:

  • Osvaldo Simeone
  • Abu Sebastian
  • Prabodh Katti
  • Sabina Spiga
  • Damien Querlioz

The following are some recent publications authored or coauthored by Rajendran, with publication year and venue noted:

  • Accurate deep neural network inference using computational phase-change memory, 2020, Repository for Publications and Research Data (ETH Zurich)
  • Memristors-From In-Memory Computing, Deep Learning Acceleration, and Spiking Neural Networks to the Future of Neuromorphic and Bio-Inspired Computing, 2020, Advanced Intelligent Systems
  • Mixed-Precision Deep Learning Based on Computational Memory, 2020, Frontiers in Neuroscience
  • Experimental Demonstration of Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses, 2020, Scientific Reports
  • Spiking Generative Adversarial Networks With a Neural Network Discriminator: Local Training, Bayesian Models, and Continual Meta-Learning, 2022, IEEE Transactions on Computers

Best Publications

  • Phase Change Memory

    H P Wong;S Raoux;S Kim;J Liang

  • Phase change memory technology

    Geoffrey W. Burr;Matthew J. Breitwisch;Michele Franceschini;Davide Garetto

  • Neuromorphic computing with multi-memristive synapses

    Irem Boybat;Irem Boybat;Manuel Le Gallo;S. R. Nandakumar;S. R. Nandakumar;Timoleon Moraitis

  • Accurate deep neural network inference using computational phase-change memory.

    Vinay Joshi;Vinay Joshi;Manuel Le Gallo;Simon Haefeli;Simon Haefeli;Irem Boybat;Irem Boybat

  • A 45nm CMOS neuromorphic chip with a scalable architecture for learning in networks of spiking neurons

    Jae-sun Seo;Bernard Brezzo;Yong Liu;Benjamin D. Parker

  • Write Strategies for 2 and 4-bit Multi-Level Phase-Change Memory

    T. Nirschl;J.B. Phipp;T.D. Happ;G.W. Burr

  • Memristors - from In-memory computing, Deep Learning Acceleration, Spiking Neural Networks, to the Future of Neuromorphic and Bio-inspired Computing.

    Adnan Mehonic;Abu Sebastian;Bipin Rajendran;Osvaldo Simeone

  • Area efficient neuromorphic system that connects a FET in a diode configuration, and a variable resistance material to junctions of neuron circuit blocks

    Matthew J. Breitwisch;Chung Hon Lam;Dharmendra S. Modha;Bipin Rajendran

  • Nanoscale electronic synapses using phase change devices

    Bryan L. Jackson;Bipin Rajendran;Gregory S. Corrado;Matthew Breitwisch

  • Specifications of Nanoscale Devices and Circuits for Neuromorphic Computational Systems

    B. Rajendran;Yong Liu;Jae-sun Seo;K. Gopalakrishnan

  • Low-Power Neuromorphic Hardware for Signal Processing Applications: A review of architectural and system-level design approaches

    Bipin Rajendran;Abu Sebastian;Michael Schmuker;Narayan Srinivasa

  • Silicon nanowires for sequence-specific DNA sensing: device fabrication and simulation

    Z. Li;Bipin Rajendran;T. I. Kamins;X. Li

  • Spiking neural networks for handwritten digit recognition—Supervised learning and network optimization

    Shruti R. Kulkarni;Bipin Rajendran

  • A phase-change memory model for neuromorphic computing

    S. R. Nandakumar;S. R. Nandakumar;Manuel Le Gallo;Irem Boybat;Irem Boybat;Bipin Rajendran

  • Phase Change Memory: From Devices to Systems

    Moinuddin K. Qureshi;Sudhanva Gurumurthi;Bipin Rajendran

  • Novel Lithography-Independent Pore Phase Change Memory

    M. Breitwisch;T. Nirschl;C.F. Chen;Y. Zhu

  • Resistive memory devices having a not-and(nand) structure

    Matthew J. Breitwisch;Gary S. Ditlow;Michele M. Franceschini;Luis A. Lastras-Montano

  • Hardware analog-digital neural networks

    Bruce G. Elmegreen;Ralph Linsker;Dennis M. Newns;Bipin Rajendran

  • CMOS transistor processing compatible with monolithic 3-D integration

    Bipin Rajendran;R. S. Shenoy;D. J. Witte;N. S. Chokshi

  • Efficient scrub mechanisms for error-prone emerging memories

    Manu Awasthi;Manjunath Shevgoor;Kshitij Sudan;Bipin Rajendran

Frequent Co-Authors

Matthew J. Breitwisch
Matthew J. Breitwisch IBM (United States)
Abu Sebastian
Abu Sebastian IBM Research - Zurich
Chung H. Lam
Chung H. Lam IBM (United States)
Simone Raoux
Simone Raoux Helmholtz-Zentrum Berlin für Materialien und Energie
Osvaldo Simeone
Osvaldo Simeone Northeastern University
Hsiang-Lan Lung
Hsiang-Lan Lung Macronix International (Taiwan)
Daniel J. Friedman
Daniel J. Friedman IBM (United States)
Erh-Kun Lai
Erh-Kun Lai Macronix International (Taiwan)
Min Yang
Min Yang Sterne, Kessler, Goldstein & Fox

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