The Experts below are selected from a list of 13413 Experts worldwide ranked by ideXlab platform
Luming Duan - One of the best experts on this subject based on the ideXlab platform.
-
experimental realization of a multiplexed quantum Memory with 225 individually Accessible Memory cells
Nature Communications, 2017Co-Authors: Nan Jiang, Wei Chang, Haozhou Yang, Luming DuanAbstract:To realize long-distance quantum communication and quantum network, it is required to have multiplexed quantum Memory with many Memory cells. Each Memory cell needs to be individually addressable and independently Accessible. Here we report an experiment that realizes a multiplexed DLCZ-type quantum Memory with 225 individually Accessible Memory cells in a macroscopic atomic ensemble. As a key element for quantum repeaters, we demonstrate that entanglement with flying optical qubits can be stored into any neighboring Memory cells and read out after a programmable time with high fidelity. Experimental realization of a multiplexed quantum Memory with many individually Accessible Memory cells and programmable control of its addressing and readout makes an important step for its application in quantum information technology.
Mathews Nripan - One of the best experts on this subject based on the ideXlab platform.
-
Ultralow power dual-gated subthreshold oxide neuristors : an enabler for higher order neuronal temporal correlations
'American Chemical Society (ACS)', 2018Co-Authors: John, Rohit Abharam, Chen Yaoyi, Tiwari Nidhi, Tiwari Naveen, Kulkarni Mohit, Nirmal Amoolya, Nguyen, Anh Chien, Basu Arindam, Mathews NripanAbstract:Inspired by neural computing, the pursuit of ultralow power neuromorphic architectures with highly distributed Memory and parallel processing capability has recently gained more traction. However, emulation of biological signal processing via artificial neuromorphic architectures does not exploit the immense interplay between local activities and global neuromodulations observed in biological neural networks and hence are unable to mimic complex biologically plausible adaptive functions like heterosynaptic plasticity and homeostasis. Here, we demonstrate emulation of complex neuronal behaviors like heterosynaptic plasticity, homeostasis, association, correlation, and coincidence in a single neuristor via a dual-gated architecture. This multiple gating approach allows one gate to capture the effect of local activity correlations and the second gate to represent global neuromodulations, allowing additional modulations which augment their plasticity, enabling higher order temporal correlations at a unitary level. Moreover, the dual-gate operation extends the available dynamic range of synaptic conductance while maintaining symmetry in the weight-update operation, expanding the number of Accessible Memory states. Finally, operating neuristors in the subthreshold regime enable synaptic weight changes with high gain while maintaining ultralow power consumption of the order of femto-Joules.MOE (Min. of Education, S’pore)Accepted versio
Nripan Mathews - One of the best experts on this subject based on the ideXlab platform.
-
Ultralow Power Dual-Gated Subthreshold Oxide Neuristors: An Enabler for Higher Order Neuronal Temporal Correlations
2018Co-Authors: Rohit Abraham John, Nidhi Tiwari, Chen Yaoyi, Naveen Tiwari, Amoolya Nirmal, Anh Chien Nguyen, Arindam Basu, Mohit Kulkarni, Nripan MathewsAbstract:Inspired by neural computing, the pursuit of ultralow power neuromorphic architectures with highly distributed Memory and parallel processing capability has recently gained more traction. However, emulation of biological signal processing via artificial neuromorphic architectures does not exploit the immense interplay between local activities and global neuromodulations observed in biological neural networks and hence are unable to mimic complex biologically plausible adaptive functions like heterosynaptic plasticity and homeostasis. Here, we demonstrate emulation of complex neuronal behaviors like heterosynaptic plasticity, homeostasis, association, correlation, and coincidence in a single neuristor via a dual-gated architecture. This multiple gating approach allows one gate to capture the effect of local activity correlations and the second gate to represent global neuromodulations, allowing additional modulations which augment their plasticity, enabling higher order temporal correlations at a unitary level. Moreover, the dual-gate operation extends the available dynamic range of synaptic conductance while maintaining symmetry in the weight-update operation, expanding the number of Accessible Memory states. Finally, operating neuristors in the subthreshold regime enable synaptic weight changes with high gain while maintaining ultralow power consumption of the order of femto-Joules
Chen Yaoyi - One of the best experts on this subject based on the ideXlab platform.
-
Ultralow power dual-gated subthreshold oxide neuristors : an enabler for higher order neuronal temporal correlations
'American Chemical Society (ACS)', 2018Co-Authors: John, Rohit Abharam, Chen Yaoyi, Tiwari Nidhi, Tiwari Naveen, Kulkarni Mohit, Nirmal Amoolya, Nguyen, Anh Chien, Basu Arindam, Mathews NripanAbstract:Inspired by neural computing, the pursuit of ultralow power neuromorphic architectures with highly distributed Memory and parallel processing capability has recently gained more traction. However, emulation of biological signal processing via artificial neuromorphic architectures does not exploit the immense interplay between local activities and global neuromodulations observed in biological neural networks and hence are unable to mimic complex biologically plausible adaptive functions like heterosynaptic plasticity and homeostasis. Here, we demonstrate emulation of complex neuronal behaviors like heterosynaptic plasticity, homeostasis, association, correlation, and coincidence in a single neuristor via a dual-gated architecture. This multiple gating approach allows one gate to capture the effect of local activity correlations and the second gate to represent global neuromodulations, allowing additional modulations which augment their plasticity, enabling higher order temporal correlations at a unitary level. Moreover, the dual-gate operation extends the available dynamic range of synaptic conductance while maintaining symmetry in the weight-update operation, expanding the number of Accessible Memory states. Finally, operating neuristors in the subthreshold regime enable synaptic weight changes with high gain while maintaining ultralow power consumption of the order of femto-Joules.MOE (Min. of Education, S’pore)Accepted versio
-
Ultralow Power Dual-Gated Subthreshold Oxide Neuristors: An Enabler for Higher Order Neuronal Temporal Correlations
2018Co-Authors: Rohit Abraham John, Nidhi Tiwari, Chen Yaoyi, Naveen Tiwari, Amoolya Nirmal, Anh Chien Nguyen, Arindam Basu, Mohit Kulkarni, Nripan MathewsAbstract:Inspired by neural computing, the pursuit of ultralow power neuromorphic architectures with highly distributed Memory and parallel processing capability has recently gained more traction. However, emulation of biological signal processing via artificial neuromorphic architectures does not exploit the immense interplay between local activities and global neuromodulations observed in biological neural networks and hence are unable to mimic complex biologically plausible adaptive functions like heterosynaptic plasticity and homeostasis. Here, we demonstrate emulation of complex neuronal behaviors like heterosynaptic plasticity, homeostasis, association, correlation, and coincidence in a single neuristor via a dual-gated architecture. This multiple gating approach allows one gate to capture the effect of local activity correlations and the second gate to represent global neuromodulations, allowing additional modulations which augment their plasticity, enabling higher order temporal correlations at a unitary level. Moreover, the dual-gate operation extends the available dynamic range of synaptic conductance while maintaining symmetry in the weight-update operation, expanding the number of Accessible Memory states. Finally, operating neuristors in the subthreshold regime enable synaptic weight changes with high gain while maintaining ultralow power consumption of the order of femto-Joules
Keshav Pingali - One of the best experts on this subject based on the ideXlab platform.
-
Single Machine Graph Analytics on Massive Datasets Using Intel Optane DC Persistent Memory.
arXiv: Distributed Parallel and Cluster Computing, 2019Co-Authors: Gurbinder Gill, Roshan Dathathri, Loc Hoang, Ramesh Peri, Keshav PingaliAbstract:Intel Optane DC Persistent Memory (Optane PMM) is a new kind of byte-addressable Memory with higher density and lower cost than DRAM. This enables the design of affordable systems that support up to 6TB of randomly Accessible Memory. In this paper, we present key runtime and algorithmic principles to consider when performing graph analytics on extreme-scale graphs on large-Memory platforms of this sort. To demonstrate the importance of these principles, we evaluate four existing shared-Memory graph frameworks on large real-world web-crawls, using a machine with 6TB of Optane PMM. Our results show that frameworks based on the runtime and algorithmic principles advocated in this paper (i) perform significantly better than the others, and (ii) are competitive with graph analytics frameworks running on large production clusters.
