M
Manan Suri
Researcher at Indian Institute of Technology Delhi
Publications - 123
Citations - 2739
Manan Suri is an academic researcher from Indian Institute of Technology Delhi. The author has contributed to research in topics: Computer science & Neuromorphic engineering. The author has an hindex of 17, co-authored 95 publications receiving 1992 citations. Previous affiliations of Manan Suri include Alternatives & French Alternative Energies and Atomic Energy Commission.
Papers
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Journal ArticleDOI
SLIM: Simultaneous Logic-in-Memory Computing Exploiting Bilayer Analog OxRAM Devices.
TL;DR: This paper proposes a novel ‘Simultaneous Logic in-Memory’ (SLIM) methodology which is complementary to existing LIM approaches in literature and demonstrates novel SLIM bitcells comprising non-filamentary bilayer analog OxRAM devices with NMOS transistors.
Journal ArticleDOI
SLIM: Simultaneous Logic-in-Memory Computing Exploiting Bilayer Analog OxRAM Devices
TL;DR: In this paper, the authors proposed a novel "simultaneous logic in-memory" (SLIM) methodology that allows to implement both memory and logic operations simultaneously on the same bitcell in a non-destructive manner without losing the previously stored Memory state.
Proceedings ArticleDOI
Phase change memory as synapse for ultra-dense neuromorphic systems: Application to complex visual pattern extraction
Manan Suri,Olivier Bichler,Damien Querlioz,O. Cueto,Luca Perniola,Veronique Sousa,Dominique Vuillaume,Christian Gamrat,B. DeSalvo +8 more
TL;DR: A versatile behavioral model of PCM which can be used for simulating large scale neural systems is introduced and first demonstration of complex visual pattern extraction from real world data using PCM synapses in a 2-layer spiking neural network (SNN) is shown.
Journal ArticleDOI
Bio-Inspired Stochastic Computing Using Binary CBRAM Synapses
Manan Suri,Damien Querlioz,Olivier Bichler,Giorgio Palma,Elisa Vianello,Dominique Vuillaume,Christian Gamrat,B. DeSalvo +7 more
TL;DR: An original methodology to use conductive-bridge RAM (CBRAM) devices as, easy to program and low-power, binary synapses with stochastic learning rules with deterministic learning rules is demonstrated.
Journal ArticleDOI
Visual Pattern Extraction Using Energy-Efficient “2-PCM Synapse” Neuromorphic Architecture
TL;DR: This work introduces a novel energy-efficient methodology “2-PCM Synapse” to use phase-change memory (PCM) as synapses in large-scale neuromorphic systems and exploits the gradual crystallization behavior of PCM devices for emulating both synaptic potentiation and synaptic depression.