Resonant tunnelling diode nano-optoelectronic spiking nodes for neuromorphic information processing

• URL: http://arxiv.org/abs/2107.06721v3
• Date: Fri, 19 Nov 2021 12:58:12 GMT
• Title: Resonant tunnelling diode nano-optoelectronic spiking nodes for neuromorphic information processing
• Authors: Mat\v{e}j Hejda, Juan Arturo Alanis, Ignacio Ortega-Piwonka, Jo\~ao Louren\c{c}o, Jos\'e Figueiredo, Julien Javaloyes, Bruno Romeira and Antonio Hurtado
• Abstract summary: We introduce an optoelectronic artificial neuron capable of operating at ultrafast rates and with low energy consumption. The proposed system combines an excitable tunnelling diode (RTD) element with a nanoscale light source.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we introduce an optoelectronic spiking artificial neuron capable of operating at ultrafast rates ($\approx$ 100 ps/optical spike) and with low energy consumption ($<$ pJ/spike). The proposed system combines an excitable resonant tunnelling diode (RTD) element exhibiting negative differential conductance, coupled to a nanoscale light source (forming a master node) or a photodetector (forming a receiver node). We study numerically the spiking dynamical responses and information propagation functionality of an interconnected master-receiver RTD node system. Using the key functionality of pulse thresholding and integration, we utilize a single node to classify sequential pulse patterns and perform convolutional functionality for image feature (edge) recognition. We also demonstrate an optically-interconnected spiking neural network model for processing of spatiotemporal data at over 10 Gbps with high inference accuracy. Finally, we demonstrate an off-chip supervised learning approach utilizing spike-timing dependent plasticity for the RTD-enabled photonic spiking neural network. These results demonstrate the potential and viability of RTD spiking nodes for low footprint, low energy, high-speed optoelectronic realization of neuromorphic hardware.

Related papers

• Single Neuromorphic Memristor closely Emulates Multiple Synaptic Mechanisms for Energy Efficient Neural Networks [71.79257685917058]
  We demonstrate memristive nano-devices based on SrTiO3 that inherently emulate all these synaptic functions. These memristors operate in a non-filamentary, low conductance regime, which enables stable and energy efficient operation.
  arXiv  Detail & Related papers  (2024-02-26T15:01:54Z)
• Scalable Superconductor Neuron with Ternary Synaptic Connections for Ultra-Fast SNN Hardware [4.216765320139095]
  A novel high-fan-in differential superconductor neuron structure is designed for ultra-high-performance Spiking Neural Network (SNN) accelerators. The proposed neuron design is based on superconductor electronics fabric, incorporating multiple superconducting loops, each with two Josephson Junctions. The network exhibits a remarkable throughput of 8.92 GHz while consuming only 1.5 nJ per inference, including the energy consumption associated with cooling to 4K.
  arXiv  Detail & Related papers  (2024-02-26T08:10:14Z)
• Design and simulation of a transmon qubit chip for Axion detection [103.69390312201169]
  Device based on superconducting qubits has been successfully applied in detecting few-GHz single photons via Quantum Non-Demolition measurement (QND) In this study, we present Qub-IT's status towards the realization of its first superconducting qubit device.
  arXiv  Detail & Related papers  (2023-10-08T17:11:42Z)
• Optically-triggered deterministic spiking regimes in nanostructure resonant tunnelling diode-photodetectors [0.21550839871882013]
  This work reports a nanostructure resonant tunnelling diode-photodetector (RTD-PD) device and demonstrates its operation as a controllable, optically-triggered spike generator. We demonstrate experimentally the deterministic optical triggering of controllable and repeatable neuron-like spike patterns in the nanostructure RTD-PDs.
  arXiv  Detail & Related papers  (2023-04-23T18:00:54Z)
• Artificial optoelectronic spiking neuron based on a resonant tunnelling diode coupled to a vertical cavity surface emitting laser [0.17354071459927545]
  Excitable optoelectronic devices represent one of the key building blocks for implementation of artificial spiking neurons. This work introduces and experimentally investigates an opto-electro-optical (O/E/O) artificial neuron built with a resonant tunnelling diode (RTD) We demonstrate a well defined excitability threshold, above which this neuron produces 100 ns optical spiking responses with characteristic neural-like refractory period.
  arXiv  Detail & Related papers  (2022-06-22T14:43:03Z)
• All-optical graph representation learning using integrated diffractive photonic computing units [51.15389025760809]
  Photonic neural networks perform brain-inspired computations using photons instead of electrons. We propose an all-optical graph representation learning architecture, termed diffractive graph neural network (DGNN) We demonstrate the use of DGNN extracted features for node and graph-level classification tasks with benchmark databases and achieve superior performance.
  arXiv  Detail & Related papers  (2022-04-23T02:29:48Z)
• Event-based Video Reconstruction via Potential-assisted Spiking Neural Network [48.88510552931186]
  Bio-inspired neural networks can potentially lead to greater computational efficiency on event-driven hardware. We propose a novel Event-based Video reconstruction framework based on a fully Spiking Neural Network (EVSNN) We find that the spiking neurons have the potential to store useful temporal information (memory) to complete such time-dependent tasks.
  arXiv  Detail & Related papers  (2022-01-25T02:05:20Z)
• Tunable Anderson Localization of Dark States [146.2730735143614]
  We experimentally study Anderson localization in a superconducting waveguide quantum electrodynamics system. We observe an exponential suppression of the transmission coefficient in the vicinity of its subradiant dark modes. The experiment opens the door to the study of various localization phenomena on a new platform.
  arXiv  Detail & Related papers  (2021-05-25T07:52:52Z)
• Izhikevich-Inspired Optoelectronic Neurons with Excitatory and Inhibitory Inputs for Energy-Efficient Photonic Spiking Neural Networks [0.0]
  We develop a detailed optoelectronic neuron model in Verilog-A. We conduct simulations using fully connected (FC) and convolutional neural networks (CNN)
  arXiv  Detail & Related papers  (2021-05-03T03:58:03Z)
• A superconducting nanowire spiking element for neural networks [0.0]
  Key to the success of largescale neural networks is a power-efficient spiking element that is scalable and easily interfaced with traditional control electronics. We present a spiking element fabricated from superconducting nanowires that has pulse energies on the order of 10 aJ. We demonstrate that the device reproduces essential characteristics of biological neurons, such as a refractory period and a firing threshold.
  arXiv  Detail & Related papers  (2020-07-29T20:48:36Z)
• Flexible Transmitter Network [84.90891046882213]
  Current neural networks are mostly built upon the MP model, which usually formulates the neuron as executing an activation function on the real-valued weighted aggregation of signals received from other neurons. We propose the Flexible Transmitter (FT) model, a novel bio-plausible neuron model with flexible synaptic plasticity. We present the Flexible Transmitter Network (FTNet), which is built on the most common fully-connected feed-forward architecture.
  arXiv  Detail & Related papers  (2020-04-08T06:55:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.