S
Severin Sidler
Researcher at IBM
Publications - 16
Citations - 2598
Severin Sidler is an academic researcher from IBM. The author has contributed to research in topics: Neuromorphic engineering & Artificial neural network. The author has an hindex of 9, co-authored 16 publications receiving 1972 citations. Previous affiliations of Severin Sidler include École Polytechnique Fédérale de Lausanne.
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Journal ArticleDOI
Neuromorphic computing using non-volatile memory
Geoffrey W. Burr,Robert M. Shelby,Abu Sebastian,Sangbum Kim,Seyoung Kim,Severin Sidler,Kumar Virwani,Masatoshi Ishii,Pritish Narayanan,Alessandro Fumarola,Lucas L. Sanches,Irem Boybat,Manuel Le Gallo,Kibong Moon,Jiyoo Woo,Hyunsang Hwang,Yusuf Leblebici +16 more
TL;DR: The relevant virtues and limitations of these devices are assessed, in terms of properties such as conductance dynamic range, (non)linearity and (a)symmetry of conductance response, retention, endurance, required switching power, and device variability.
Journal ArticleDOI
Experimental Demonstration and Tolerancing of a Large-Scale Neural Network (165 000 Synapses) Using Phase-Change Memory as the Synaptic Weight Element
Geoffrey W. Burr,Robert M. Shelby,Severin Sidler,Carmelo di Nolfo,Junwoo Jang,Irem Boybat,Rohit S. Shenoy,Pritish Narayanan,Kumar Virwani,E.U. Giacometti,B. N. Kurdi,Hyunsang Hwang +11 more
TL;DR: Using 2 phase-change memory devices per synapse, a 3-layer perceptron network is trained on a subset of the MNIST database of handwritten digits using a backpropagation variant suitable for NVM+selector crossbar arrays, obtaining a training (generalization) accuracy of 82.2%.
Journal ArticleDOI
Equivalent-accuracy accelerated neural-network training using analogue memory
Stefano Ambrogio,Pritish Narayanan,Hsinyu Tsai,Robert M. Shelby,Irem Boybat,Irem Boybat,Carmelo di Nolfo,Carmelo di Nolfo,Severin Sidler,Severin Sidler,Massimo Giordano,Martina Bodini,Martina Bodini,Nathan C. P. Farinha,Benjamin Killeen,Christina Cheng,Yassine Jaoudi,Geoffrey W. Burr +17 more
TL;DR: Mixed hardware–software neural-network implementations that involve up to 204,900 synapses and that combine long-term storage in phase-change memory, near-linear updates of volatile capacitors and weight-data transfer with ‘polarity inversion’ to cancel out inherent device-to-device variations are demonstrated.
Proceedings ArticleDOI
Large-scale neural networks implemented with non-volatile memory as the synaptic weight element: Comparative performance analysis (accuracy, speed, and power)
Geoffrey W. Burr,Pritish Narayanan,Robert M. Shelby,Severin Sidler,Irem Boybat,C. di Nolfo,Yusuf Leblebici +6 more
TL;DR: It is shown that NVM-based systems could potentially offer faster and lower-power ML training than GPU-based hardware, despite the inherent random and deterministic imperfections of such devices.
Journal ArticleDOI
Bidirectional Non-Filamentary RRAM as an Analog Neuromorphic Synapse, Part I: Al/Mo/Pr 0.7 Ca 0.3 MnO 3 Material Improvements and Device Measurements
Kibong Moon,Alessandro Fumarola,Severin Sidler,Junwoo Jang,Pritish Narayanan,Robert M. Shelby,Geoffrey W. Burr,Hyunsang Hwang +7 more
TL;DR: Improvements to non-filamentary RRAM devices based on Pr0.7Ca0.3MnO3 by introducing an MoOx buffer layer together with a reactive Al electrode, and on device measurements designed to help gauge the performance of these devices as bidirectional analog synapses for on-chip acceleration of the backpropagation algorithm.