Neuromorphic Computing is Turing-Complete

Date, Prasanna; Schuman, Catherine D.; Kay, Bill; Potok, Thomas E.

doi:10.1145/3546790.3546806

Cited by 9 publications

(10 citation statements)

References 35 publications

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“…Additional gates positioned on each lead can exert control on the charge/spin that can enter or exit the QD, while the Rashba field can control the spin-momentum coupling in the active layer. Therefore, the QI units together with the system of QD registers can serve as building blocks for non-von Neumann computing architectures, which emulate the working of the human brain and have certain advantages over the CPU and GPU systems in terms of power efficiency [34].…”

Section: Neuromorphic Electron Waveguidesmentioning

confidence: 99%

Design of Nanoscale Quantum Interconnects Aided by Conditional Generative Adversarial Networks

Preda,

Pantis-Simut,

Marciu

et al. 2024

Applied Sciences

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Interconnecting nanodevices with the aim of assembling quantum computing architectures is one of the current outstanding challenges. At the nanoscale, the quantum interconnects become comparable in complexity with the active devices and should be treated on equal footing. In addition, they can play an active role in the switching properties. Here, we investigate the charge localization in neuromorphic bi-dimensional systems, which serve as quantum interconnects (QIs) between quantum dot registers. We define a device structure where, by manipulating the charging of a floating gate array, one defines the QI potential map, which can host a few interacting electrons. The ground state charge density may be extracted by measuring the tunneling current perpendicular to the device surface, yielding a convoluted image of the electron distribution. Using image-to-image translation methods, we achieve the mapping of the charge density from the confinement potential, as well as by deconvoluting the tunneling current map, which can be obtained by a direct measurement. Thus, we provide a proof-of-concept for a reconfigurable device, which can be used to design quantum many-electron devices.

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Section: Neuromorphic Electron Waveguidesmentioning

confidence: 99%

Design of Nanoscale Quantum Interconnects Aided by Conditional Generative Adversarial Networks

Preda,

Pantis-Simut,

Marciu

et al. 2024

Applied Sciences

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“…The types of algorithms most suitable for a given neuromorphic implementation depend on the underlying architecture, devices, and materials of the implementation. Each training and learning approach has its own requirements for the neuromorphic Date et al (2022b). The neurons in this model are leaky integrate and fire (LIF) neurons that have two parameters: threshold and leak.…”

Section: Algorithmsmentioning

confidence: 99%

“…In recent years, several advancements have been made toward the general-purpose applications of neuromorphic computing. First, the Turing-completeness of neuromorphic computing has been proven-this provides a compelling theoretical argument that neuromorphic computers are capable of general-purpose computation (Date et al, 2022b). In fact, from a theoretical standpoint, neuromorphic computers can perform all those operations that today's computers can perform.…”

Section: General-purpose Computing Outlookmentioning

confidence: 99%

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Abisko: Deep codesign of an architecture for spiking neural networks using novel neuromorphic materials

Vetter

Date

Fahim

et al. 2023

The International Journal of High Performance Computing Applica

Self Cite

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The Abisko project aims to develop an energy-efficient spiking neural network (SNN) computing architecture and software system capable of autonomous learning and operation. The SNN architecture explores novel neuromorphic devices that are based on resistive-switching materials, such as memristors and electrochemical RAM. Equally important, Abisko uses a deep codesign approach to pursue this goal by engaging experts from across the entire range of disciplines: materials, devices and circuits, architectures and integration, software, and algorithms. The key objectives of our Abisko project are threefold. First, we are designing an energy-optimized high-performance neuromorphic accelerator based on SNNs. This architecture is being designed as a chiplet that can be deployed in contemporary computer architectures and we are investigating novel neuromorphic materials to improve its design. Second, we are concurrently developing a productive software stack for the neuromorphic accelerator that will also be portable to other architectures, such as field-programmable gate arrays and GPUs. Third, we are creating a new deep codesign methodology and framework for developing clear interfaces, requirements, and metrics between each level of abstraction to enable the system design to be explored and implemented interchangeably with execution, measurement, a model, or simulation. As a motivating application for this codesign effort, we target the use of SNNs for an analog event detector for a high-energy physics sensor.

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“…Neuromorphic computing uses networks of bioplausible neurons, or spiking neurons that compute with binary valued signals (called spikes). The networks of these neurons, called Spiking Neural Networks (SNNs) have an inherent notion of time embedded in their dynamics as synaptic delays and neuronal time constants [7,8]. SNNs have been demonstrated for very diverse sets of cognitive and non-cognitive applications such as autonomous navigation, anomaly detection, graph algorithms, epidemic modeling, etc.…”

Section: Introductionmentioning

confidence: 99%

On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments

R. Kulkarni,

Young,

Date

et al. 2023

Proceedings of the 2023 International Conference on Neuromorphic Systems

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In this paper, we investigate the prospects of applying neuromorphic computing spiking neural network models to filter data on the readout electronics of the sensor in the high energy physics experiments at the High Luminosity Large Hadron Collider. We present our approach on developing a compact neuromorphic model that filters out the sensor data based on the particle's transverse momentum with the goal of reducing the amount of data being sent to the downstream electronics. The incoming charge waveforms are converted to streams of binary-valued events which are processed by the SNN. We present our insights on the various system design choices from data encoding to optimal hyperparameters of the training algorithm for an accurate and compact SNN optimized for hardware deployment. Our results show that an SNN trained using an evolutionary algorithm and with optimized set of hyperparameters shows a signal efficiency of about 91% with nearly half the number of parameters than a Deep Neural Network.

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Neuromorphic Computing is Turing-Complete

Cited by 9 publications

References 35 publications

Design of Nanoscale Quantum Interconnects Aided by Conditional Generative Adversarial Networks

Design of Nanoscale Quantum Interconnects Aided by Conditional Generative Adversarial Networks

Abisko: Deep codesign of an architecture for spiking neural networks using novel neuromorphic materials

On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments

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