S. R. Nandakumar

IBM Research - Zurich — Switzerland

Engineering · Physical Sciences

19h-index2.5kcitations37works0.02yr avg
OpenAlex↗Google Scholar↗Lab page↗

Accepting Students?

No reports yet. Know if this professor is taking students?

Research Topics

Advanced Memory and Neural Computing(34), Ferroelectric and Negative Capacitance Devices(17), Neural Networks and Reservoir Computing(12), Neural dynamics and brain function(8), Neuroscience and Neural Engineering(8)

Publications37 total

Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators
Nature Communications·2023· 129 citedOpen Access
Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators
arXiv (Cornell University)·2023· 3 citedOpen Access
ML-HW Co-Design of Noise-Robust TinyML Models and Always-On Analog Compute-in-Memory Edge Accelerator
IEEE Micro·2022· 29 cited
Optimised weight programming for analogue memory-based deep neural networks
Nature Communications·2022· 46 citedOpen Access
HERMES-Core—A 1.59-TOPS/mm<sup>2</sup> PCM on 14-nm CMOS In-Memory Compute Core Using 300-ps/LSB Linearized CCO-Based ADCs
IEEE Journal of Solid-State Circuits·2022· 122 citedOpen Access
Precision of bit slicing with in-memory computing based on analog phase-change memory crossbars
Neuromorphic Computing and Engineering·2022· 39 citedOpen Access
AnalogNets: ML-HW Co-Design of Noise-robust TinyML Models and Always-On Analog Compute-in-Memory Accelerator
arXiv (Cornell University)·2021· 8 citedOpen Access
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
2021· 110 cited
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
2021
Accurate Weight Mapping in a Multi-Memristive Synaptic Unit
2021· 3 cited
Mushroom-Type phase change memory with projection liner: An array-level demonstration of conductance drift and noise mitigation
2021· 20 cited
Precision of synaptic weights programmed in phase-change memory devices for deep learning inference
2020· 33 cited
Experimental Demonstration of Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses
Scientific Reports·2020· 77 citedOpen Access
Mixed-Precision Deep Learning Based on Computational Memory
Frontiers in Neuroscience·2020· 114 citedOpen Access
Bio-mimetic synaptic plasticity and learning in a sub-500 mV Cu/SiO2/W memristor
Microelectronic Engineering·2020· 14 cited
Bio-mimetic Synaptic Plasticity and Learning in a sub-500mV Cu/SiO$_2$/W Memristor
arXiv (Cornell University)·2020Open Access
Accurate deep neural network inference using computational phase-change memory
Repository for Publications and Research Data (ETH Zurich)·2020· 395 citedOpen Access
Phase-Change Memory Models for Deep Learning Training and Inference
2019· 31 citedOpen Access
Deep learning acceleration based on in-memory computing
IBM Journal of Research and Development·2019· 37 cited
Computational memory-based inference and training of deep neural networks
2019· 18 citedOpen Access
page 1 of 2Next →

Frequent Co-authors

Manuel Le Gallo(27), Abu Sebastian(27), Bipin Rajendran(23), Irem Boybat(21), Evangelos Eleftheriou(18), Riduan Khaddam-Aljameh(9), Geethan Karunaratne(7), Vinay Joshi(6), Christophe Piveteau(6), Vijay Narayanan(6), Theodore Antonakopoulos(6), Charles Mackin(5), Urs Egger(5), Benedikt Kersting(5), Martino Dazzi(4), Malte J. Rasch(4), Hsinyu Tsai(4), Geoffrey W. Burr(4), Miloš Stanisavljević(4), Kevin Brew(4)