Special Issue "Neuromorphic Sensing and Computing Systems"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Circuit and Signal Processing".

Deadline for manuscript submissions: 31 January 2022.

Special Issue Editors

Dr. Federico Corradi
E-Mail Website
Guest Editor
Ultra Low Power Systems for IoT, Stichting IMEC Nederland, Eindhoven, The Netherlands
Interests: neuromorphic engineering; bio-signal processing; neuroscience; on-line learning; edge computing; embedded systems
Dr. Anup Das
E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA 19104, USA
Interests: computer architectures; neuromorphic computing; in-memory computing; non-volatile memories

Special Issue Information

Dear Colleagues,

Neuromorphic computing is currently being proposed as an alternative and efficient way to carry out computation using principles derived from neuro-biological systems. Although the neuromorphic term has historically been used to describe hardware implementations of neural circuits in analog, digital, or mixed-mode analog/digital VLSI, in recent years, it has also been used to describe a wider spectrum of sensing and computing systems. These systems sometimes include emerging memories, and alternative neuron and synapse technologies. In all cases, the application of neuromorphic systems faces the challenge of building novel algorithms, tools, and architectures that can best cope with the nature of low-power, dense, and parallel elements. The complexity and sophistication of such systems is increasing over time with an unprecedented speed both at the theoretical and technological level.

Thus, in this Special Issue, we aim to start a discussion about the state of the art in neuromorphic sensing and computing systems, analyzing architectures, algorithms, and their potential impact in a broad spectrum of applications.

For this purpose, this Special Issue is open to receiving a variety of meaningful and valuable manuscripts concerning the topic of neuromorphic sensing and computing systems. We welcome work related to hardware architectures, event-based sensing and computing, spiking neural networks, learning systems, and alternative neuromorphic computing paradigms. We will also consider submissions that involve emerging memories and unconventional computing technologies as candidate solutions for the execution of neural information processing in an extremely efficient way.

Dr. Federico Corradi
Dr. Anup Das
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Analog/digital/mixed-signal circuits and architectures for neuromorphic systems
  • Architectures and algorithms for neuromorphic computing
  • Spiking neural networks
  • Bio-inspired signal processing
  • Neuro mimicking materials and principles
  • Event-based sensory systems, spike-based processing
  • On-line, real-time, edge computing
  • Learning systems
  • High performance neuromorphic computing systems and architectures
  • Spintronics, memristors, carbon nanotubes, photonics

Published Papers (3 papers)

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Research

Article
EDHA: Event-Driven High Accurate Simulator for Spike Neural Networks
Electronics 2021, 10(18), 2281; https://doi.org/10.3390/electronics10182281 - 17 Sep 2021
Viewed by 194
Abstract
In recent years, spiking neural networks (SNNs) have attracted increasingly more researchers to study by virtue of its bio-interpretability and low-power computing. The SNN simulator is an essential tool to accomplish image classification, recognition, speech recognition, and other tasks using SNN. However, most [...] Read more.
In recent years, spiking neural networks (SNNs) have attracted increasingly more researchers to study by virtue of its bio-interpretability and low-power computing. The SNN simulator is an essential tool to accomplish image classification, recognition, speech recognition, and other tasks using SNN. However, most of the existing simulators for spike neural networks are clock-driven, which has two main problems. First, the calculation result is affected by time slice, which obviously shows that when the calculation accuracy is low, the calculation speed is fast, but when the calculation accuracy is high, the calculation speed is unacceptable. The other is the failure of lateral inhibition, which severely affects SNN learning. In order to solve these problems, an event-driven high accurate simulator named EDHA (Event-Driven High Accuracy) for spike neural networks is proposed in this paper. EDHA takes full advantage of the event-driven characteristics of SNN and only calculates when a spike is generated, which is independent of the time slice. Compared with previous SNN simulators, EDHA is completely event-driven, which reduces a large amount of calculations and achieves higher computational accuracy. The calculation speed of EDHA in the MNIST classification task is more than 10 times faster than that of mainstream clock-driven simulators. By optimizing the spike encoding method, the former can even achieve more than 100 times faster than the latter. Due to the cross-platform characteristics of Java, EDHA can run on x86, amd64, ARM, and other platforms that support Java. Full article
(This article belongs to the Special Issue Neuromorphic Sensing and Computing Systems)
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Article
LogicSNN: A Unified Spiking Neural Networks Logical Operation Paradigm
Electronics 2021, 10(17), 2123; https://doi.org/10.3390/electronics10172123 - 31 Aug 2021
Viewed by 366
Abstract
LogicSNN, a unified spiking neural networks (SNN) logical operation paradigm is proposed in this paper. First, we define the logical variables under the semantics of SNN. Then, we design the network structure of this paradigm and use spike-timing-dependent plasticity for training. According to [...] Read more.
LogicSNN, a unified spiking neural networks (SNN) logical operation paradigm is proposed in this paper. First, we define the logical variables under the semantics of SNN. Then, we design the network structure of this paradigm and use spike-timing-dependent plasticity for training. According to this paradigm, six kinds of basic SNN binary logical operation modules and three kinds of combined logical networks based on these basic modules are implemented. Through these experiments, the rationality, cascading characteristics and the potential of building large-scale network of this paradigm are verified. This study fills in the blanks of the logical operation of SNN and provides a possible way to realize more complex machine learning capabilities. Full article
(This article belongs to the Special Issue Neuromorphic Sensing and Computing Systems)
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Article
Radar-Based Hand Gesture Recognition Using Spiking Neural Networks
Electronics 2021, 10(12), 1405; https://doi.org/10.3390/electronics10121405 - 11 Jun 2021
Viewed by 776
Abstract
We propose a spiking neural network (SNN) approach for radar-based hand gesture recognition (HGR), using frequency modulated continuous wave (FMCW) millimeter-wave radar. After pre-processing the range-Doppler or micro-Doppler radar signal, we use a signal-to-spike conversion scheme that encodes radar Doppler maps into spike [...] Read more.
We propose a spiking neural network (SNN) approach for radar-based hand gesture recognition (HGR), using frequency modulated continuous wave (FMCW) millimeter-wave radar. After pre-processing the range-Doppler or micro-Doppler radar signal, we use a signal-to-spike conversion scheme that encodes radar Doppler maps into spike trains. The spike trains are fed into a spiking recurrent neural network, a liquid state machine (LSM). The readout spike signal from the SNN is then used as input for different classifiers for comparison, including logistic regression, random forest, and support vector machine (SVM). Using liquid state machines of less than 1000 neurons, we achieve better than state-of-the-art results on two publicly available reference datasets, reaching over 98% accuracy on 10-fold cross-validation for both data sets. Full article
(This article belongs to the Special Issue Neuromorphic Sensing and Computing Systems)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Planned Paper 1. Direct Spike Encoding Circuit for Audio Signal

Abstract: The lack of spike encoding circuits for audio signals limits the application of neuromorphic computing methods represented by spike neural networks (SNN) in audio signal processing. Four essential
processes are included in the traditional spike encoding circuits for audio signals: acquisition of audio signal, analog to digital conversion, time to frequency domain conversion, and spike encoding. Due to the software calculation circuit for time to frequency domain conversion and spike encoding in traditional spike encoding circuits for audio signals, the traditional method has a certain conversion delay. In addition, the encoding result is highly dependent on the sampling accuracy and speed of the analog to digital converter. This paper proposes DSEC: a direct spike encoding circuit for audio signals, which contains audio signal acquisition, band-pass filtering, and spike encoding. Analog to digital conversion is not contained and all processes in DSEC are completely implemented by hardware circuits, which speeds up the input response significantly.

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