Query: “Superconducting optoelectronic circuits for neuromorphic computing

Candidate papers:

abstract: Neural networks have proven effective for solving many difficult computational problems. Implementing complex neural networks in software is very computationally expensive. To explore the limits of information processing, it will be necessary to implement new hardware platforms with large numbers of neurons, each with a large number of connections to other neurons. Here we propose a hybrid semiconductor superconductor hardware platform for the implementation of neural networks and large scale neuromorphic computing. The platform combines semiconducting few photon light emitting diodes with superconducting nanowire single photon detectors to behave as spiking neurons. These processing units are connected via a network of optical waveguides, and variable weights of connection can be implemented using several approaches. The use of light as a signaling mechanism overcomes fanout and parasitic constraints on electrical signals while simultaneously introducing physical degrees of freedom which can be employed for computation. The use of supercurrents achieves the low power density necessary to scale to systems with enormous entropy. The proposed processing units can operate at speeds of at least $20$ MHz with fully asynchronous activity, light speed limited latency, and power densities on the order of 1 mW/cm$^2$ for neurons with 700 connections operating at full speed at 2 K. The processing units achieve an energy efficiency of \approx 20$ aJ per synapse event. By leveraging multilayer photonics with deposited waveguides and superconductors with feature sizes 100 nm, this approach could scale to systems with massive interconnectivity and complexity for advanced computing as well as explorations of information processing capacity in systems with an enormous number of information bearing microstates.
author_names: Jeffrey M Shainline

Sonia M Buckley

Richard P Mirin

Sae Woo Nam
title: Superconducting optoelectronic circuits for neuromorphic computing
venue: ArXiv
year: 2016

abstract: We have previously proposed a novel hardware platform (SOEN) for neuromorphic computing based on superconducting optoelectronics that presents many of the features necessary for information processing in the brain. Here we discuss the design and training of networks of neurons and synapses based on this technology. We present circuit models for the simplest neurons and synapses that we can use to build networks. We discuss the further abstracted integrate and fire model that we use for evolutionary optimization of small networks of these neurons. We show that we can use the TENNLab evolutionary optimization programming framework to design small networks for logic, control and classification tasks. We plan to use the results as feedback to inform our neuron design.
author_names: Sonia M Buckley

Adam Nykoruk McCaughan

Jeff Chiles

Richard P Mirin

Sae Woo Nam

Jeffrey M Shainline

Grant Bruer

James S Plank

Catherine D Schuman
title: Design of Superconducting Optoelectronic Networks for Neuromorphic Computing
venue: 2018 IEEE International Conference on Rebooting Computing (ICRC)
year: 2018

abstract: We have previously proposed a novel hardware platform (SOEN) for neuromorphic computing based on superconducting optoelectronics that presents many of the features necessary for information processing in the brain. Here we discuss the design and training of networks of neurons and synapses based on this technology. We present circuit models for the simplest neurons and synapses that we can use to build networks. We discuss the further abstracted integrate and fire model that we use for evolutionary optimization of small networks of these neurons. We show that we can use the TENNLab evolutionary optimization programming framework to design small networks for logic, control and classification tasks. We plan to use the results as feedback to inform our neuron design.
author_names: Sonia M Buckley

Adam Nykoruk McCaughan

Jeff Chiles

Richard P Mirin

Sae Woo Nam

Jeffrey M Shainline

Grant Bruer

James S Plank

Catherine D Schuman
title: Design of Superconducting Optoelectronic Networks for Neuromorphic Computing
year: 2018

abstract: We propose a hybrid semiconductor superconductor hardware platform for the implementation of neural networks and large scale neuromorphic computing. The platform combines semiconducting few photon light emitting diodes with superconducting nanowire single photon detectors to behave as spiking neurons.
author_names: Sonia M Buckley

Adam Nykoruk McCaughan

Jeff Chiles

Richard P Mirin

Sae Woo Nam

Jeffrey M Shainline
title: Superconducting optoelectronic platform for neuromorphic computing
venue: 2017 Conference on Lasers and Electro Optics (CLEO)
year: 2017

abstract: Abstract Optically stimulated synaptic devices are critical to the development of neuromorphic computing with broad bandwidth and efficient interconnect. Although a few interesting materials have been employed to fabricate optically stimulated synaptic devices, the use of silicon (Si) that is the material of choice for very large scale integration circuits in the conventional von Neumann computing has not been explored for optically stimulated synaptic devices. Here we take advantage of one of the most important nanostructures of Si Si nanocrystals (NCs) to make synaptic devices, which can be effectively stimulated by light in the unprecedented broad spectral region from the ultraviolet to near infrared, approaching the wavelength of 2 um. These optically stimulated Si NC based synaptic devices demonstrate a series of important synaptic functionalities, well mimicking biological synapses. The plasticity of Si NC based synaptic devices originates from the dynamic trapping and release of photogenerated carriers at defects such as dangling bonds at the NC surface. The current facile use of Si NCs in broadband optoelectronic synaptic devices with low energy consumption has important implication for the large scale deployment of Si in the emerging neuromorphic computing.
author_names: Hua Tan

Zhenyi Ni

Wenbing Peng

Sichao Du

Xiangkai Liu

Shuangyi Zhao

Wei Li

Zhizhen Ye

Mingsheng Xu

Yang Xu

Xiaodong Pi

Deren Yang
title: Broadband optoelectronic synaptic devices based on silicon nanocrystals for neuromorphic computing
year: 2018

abstract: Superconducting circuits based on quantum phase slip junctions (QPSJs) can conduct quantized charge pulses, which naturally resemble action potentials generated by biological neurons. A corresponding synaptic circuit, which works as a weighted connection between two neurons, can also be realized by circuits comprised of QPSJs and magnetic Josephson junctions (MJJs) as a means of charge modulation for quantized charge propagation. In this paper, we present basic neuromorphic computing circuitry components, such as neuron and synaptic circuits, based on superconducting QPSJs and MJJs. Using a SPICE model developed for QPSJs, neuron and synaptic circuits were simulated in WRSPICE to demonstrate possible operation. We provide estimates for QPSJ energy dissipation and operation speed based on calculations using simple models. Some of the challenges for implementation of this technology are also briefly discussed.
author_names: Ran Cheng

Uday S Goteti

Michael C Hamilton
title: Superconducting Neuromorphic Computing Using Quantum Phase Slip Junctions
venue: IEEE Transactions on Applied Superconductivity
year: 2019

abstract: Abstract We report an artificial optoelectronic synapse based on a copper phthalocyanine (CuPc) and para sexiphenyl (p 6P) heterojunction structure. This device features stable conductance states and their linear distribution in long term potentiation (LTP) characteristic curve formed by continuous input light pulses. These superior synaptic characteristics originate from the fact that the number of photo holes moving into the CuPc channel and photo electrons being trapped at the p 6P/dielectric interface is constant at every light pulse. A single layer neural network is theoretically formed with these optoelectronic synaptic devices and its feasibility is studied in terms of training/recognition tasks of the Modified National Institute of Standards and Technology digit image patterns. Owing to the excellent LTP characteristic and through the use of a unidirectional update method, its maximum recognition rate is as high as 78% despite the use of a single layer network. This study is expected to provide a foundation for future studies on optoelectronic synaptic devices toward the implementation of complex artificial neural networks.
author_names: Chuan Qian

Seyong Oh

Yongsuk Choi

Jia Sun

Han Huang

Junliang Yang

Yongli Gao

Jin-Hong Park

Jeong Ho Cho
title: Solar stimulated optoelectronic synapse based on organic heterojunction with linearly potentiated synaptic weight for neuromorphic computing
year: 2019

abstract: Superconducting optoelectronic hardware has been proposed for large scale neural computing. In this work, we expand upon the circuit and network designs previously introduced. We investigate circuits using superconducting single photon detectors and Josephson junctions to perform signal reception, synaptic weighting, and integration. Designs are presented for synapses and neurons that perform integration of rate coded signals as well as detect coincidence events for temporal coding. A neuron with a single integration loop can receive input from thousands of synaptic connections, and many such loops can be employed for dendritic processing. We show that a synaptic weight can be modified via a superconducting flux storage loop inductively coupled to the current bias of the synapse. Synapses with hundreds of stable states are designed. Spike timing dependent plasticity can be implemented using two photons to strengthen and two photons to weaken the synaptic weight via Hebbian type learning rules. In addition to the synaptic receiver and plasticity circuits, we describe an amplifier chain that converts the current pulse generated when a neuron reaches threshold to a voltage pulse sufficient to produce light from a semiconductor diode. This light is the signal used to communicate between neurons in the network. We analyze the performance of the elements in the amplifier chain to calculate the energy consumption per photon created. The speed of the amplification sequence allows neuronal firing up to at least 20 MHz, independent of connectivity. We consider these neurons in network configurations to investigate near term technological potential and long term physical limitations. By modeling the physical size of superconducting optoelectronic neurons, we calculate the area of these networks. A system with 8100 neurons and 330 430 total synapses will fit on a 1 x 1 cm 2 die. Systems of millions of neurons with hundreds of millions of synapses will fit on a 300 mm wafer. For multiwafer assemblies, communication at light speed enables a neuronal pool the size of a large data center 10 5 m 2) comprised of trillions of neurons with coherent oscillations at 1 MHz.Superconducting optoelectronic hardware has been proposed for large scale neural computing. In this work, we expand upon the circuit and network designs previously introduced. We investigate circuits using superconducting single photon detectors and Josephson junctions to perform signal reception, synaptic weighting, and integration. Designs are presented for synapses and neurons that perform integration of rate coded signals as well as detect coincidence events for temporal coding. A neuron with a single integration loop can receive input from thousands of synaptic connections, and many such loops can be employed for dendritic processing. We show that a synaptic weight can be modified via a superconducting flux storage loop inductively coupled to the current bias of the synapse. Synapses with hundreds of stable states are designed. Spike timing dependent plasticity can be implemented using two photons to strengthen and two photons to weaken the synaptic weight via Hebbian type learning rules. In addition.
author_names: Jeffrey M Shainline

Sonia M Buckley

Adam Nykoruk McCaughan

Jeff Chiles

Amir Jafari Salim

Manuel A Castellanos-Beltran

Christine A Donnelly

Michael L Schneider

Richard P Mirin

Sae Woo Nam
title: Superconducting optoelectronic loop neurons
venue: Journal of Applied Physics
year: 2019

abstract: Advanced neuromorphic systems require massive interconnectivity, extreme energy efficiency, and complex signaling mechanisms. Here we propose an integrated optoelectronic platform utilizing superconducting electronics with photonic signaling to enable neuromorphic computing beyond the scale of the human brain.
author_names: Sonia M Buckley

Jeff Chiles

Adam Nykoruk McCaughan

Richard P Mirin

Sae Woo Nam

Jeffrey M Shainline
title: Photonic interconnect with superconducting electronics for large scale neuromorphic computing (Invited paper)
venue: 2017 IEEE Photonics Society Summer Topical Meeting Series (SUM)
year: 2017

abstract: As one of the most important members of the two dimensional chalcogenide family, molybdenum disulphide (MoS2) has played a fundamental role in the advancement of low dimensional electronic, optoelectronic and piezoelectric designs. Here, we demonstrate a new approach to solid state synaptic transistors using two dimensional MoS2 floating gate memories. By using an extended floating gate architecture which allows the device to be operated at near ideal subthreshold swing of 77 mV/decade over four decades of drain current, we have realised a charge tunneling based synaptic memory with performance comparable to the state of the art in neuromorphic designs. The device successfully demonstrates various features of a biological synapse, including pulsed potentiation and relaxation of channel conductance, as well as spike time dependent plasticity (STDP) Our device returns excellent energy efficiency figures and provides a robust platform based on ultrathin two dimensional nanosheets for future neuromorphic applications.
author_names: Tathagata Paul

Tanweer Ahmed

Krishna Kanhaiya Tiwari

Chetan Singh Thakur

Arindam Ghosh
title: A high performance MoS2 synaptic device with floating gate engineering for Neuromorphic Computing
venue: 2D Materials
year: 2019