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Neuromorphic Computing and Engineering
Number of Followers: 1  

  This is an Open Access Journal Open Access journal
ISSN (Online) 2634-4386
Published by IOP Homepage  [23 journals]
  • Edge to quantum: hybrid quantum-spiking neural network image classifier

    • Authors: A Ajayan; A P James
      First page: 024001
      Abstract: The extreme parallelism property warrant convergence of neural networks with that of quantum computing. As the size of the network grows, the classical implementation of neural networks becomes computationally expensive and not feasible. In this paper, we propose a hybrid image classifier model using spiking neural networks (SNN) and quantum circuits that combines dynamic behaviour of SNN with the extreme parallelism offered by quantum computing. The proposed model outperforms models in comparison with spiking neural network in classical computing, and hybrid convolution neural network-quantum circuit models in terms of various performance parameters. The proposed hybrid SNN-QC model achieves an accuracy of 99.9% in comparison with CNN-QC model accuracy of 96.3%, and SNN model of accuracy 91.2% in MNIST classification task. The tests on KMNIST and CIFAR-1O also showed improvements.
      Citation: Neuromorphic Computing and Engineering
      PubDate: 2021-09-07T23:00:00Z
      DOI: 10.1088/2634-4386/ac1cec
      Issue No: Vol. 1, No. 2 (2021)
  • Dopant network processing units: towards efficient neural network
           emulators with high-capacity nanoelectronic nodes

    • Authors: Hans-Christian Ruiz-Euler; Unai Alegre-Ibarra, Bram van de Ven, Hajo Broersma, Peter A Bobbert Wilfred G van der Wiel
      First page: 024002
      Abstract: The rapidly growing computational demands of deep neural networks require novel hardware designs. Recently, tuneable nanoelectronic devices were developed based on hopping electrons through a network of dopant atoms in silicon. These ‘dopant network processing units’ (DNPUs) are highly energy-efficient and have potentially very high throughput. By adapting the control voltages applied to its electrodes, a single DNPU can solve a variety of linearly non-separable classification problems. However, using a single device has limitations due to the implicit single-node architecture. This paper presents a promising novel approach to neural information processing by introducing DNPUs as high-capacity neurons and moving from a single to a multi-neuron framework. By implementing and testing a small multi-DNPU classifier in hardware, we show that feed-forward DNPU networks improve the performance of a single DNPU from 77% to 94% test accuracy on a binary classification task with concentri...
      Citation: Neuromorphic Computing and Engineering
      PubDate: 2021-09-07T23:00:00Z
      DOI: 10.1088/2634-4386/ac1a7f
      Issue No: Vol. 1, No. 2 (2021)
  • A dual-memory architecture for reinforcement learning on neuromorphic

    • Authors: Wilkie Olin-Ammentorp; Yury Sokolov Maxim Bazhenov
      First page: 024003
      Abstract: Reinforcement learning (RL) is a foundation of learning in biological systems and provides a framework to address numerous challenges with real-world artificial intelligence applications. Efficient implementations of RL techniques could allow for agents deployed in edge-use cases to gain novel abilities, such as improved navigation, understanding complex situations and critical decision making. Toward this goal, we describe a flexible architecture to carry out RL on neuromorphic platforms. This architecture was implemented using an Intel neuromorphic processor and demonstrated solving a variety of tasks using spiking dynamics. Our study proposes a usable solution for real-world RL applications and demonstrates applicability of the neuromorphic platforms for RL problems.
      Citation: Neuromorphic Computing and Engineering
      PubDate: 2021-09-07T23:00:00Z
      DOI: 10.1088/2634-4386/ac1a64
      Issue No: Vol. 1, No. 2 (2021)
  • Introducing ‘Neuromorphic Computing and Engineering’

    • Authors: Giacomo Indiveri
      First page: 010401
      Abstract: The standard nature of computing is currently being challenged by a range of problems that start to hinder technological progress. One of the strategies being proposed to address some of these problems is to develop novel brain-inspired processing methods and technologies, and apply them to a wide range of application scenarios. This is an extremely challenging endeavor that requires researchers in multiple disciplines to combine their efforts and simultaneously co-design the processing methods, the supporting computing architectures, and their underlying technologies. The journal ‘Neuromorphic Computing and Engineering’ (NCE) has been launched to support this new community in this effort and provide a forum and repository for presenting and discussing its latest advances. Through close collaboration with our colleagues on the editorial team, the scope and characteristics of NCE have been designed to ensure it serves a growing transdisciplinary and dynamic community across acade...
      Citation: Neuromorphic Computing and Engineering
      PubDate: 2021-07-15T23:00:00Z
      DOI: 10.1088/2634-4386/ac0a5b
      Issue No: Vol. 1, No. 1 (2021)
  • 3D-aCortex: an ultra-compact energy-efficient neurocomputing platform
           based on commercial 3D-NAND flash memories

    • Authors: Mohammad Bavandpour; Shubham Sahay, Mohammad Reza Mahmoodi Dmitri B Strukov
      First page: 014001
      Abstract: We first propose an ultra-compact energy-efficient time-domain vector-by-matrix multiplier (VMM) based on commercial 3D-NAND flash memory structure. The proposed 3D-VMM uses a novel resistive successive integrate and re-scaling (RSIR) scheme to eliminate the stringent requirement of a bulky load capacitor which otherwise dominates the area- and energy-landscape of the conventional time-domain VMMs. Our rigorous analysis, performed at the 55 nm technology node, shows that RSIR-3D-VMM achieves a record-breaking area efficiency of ∼0.02 μ m 2 /Byte and the energy efficiency of ∼6 f J/Op for a 500 × 500 4-bit VMM, representing 5× and 1.3× improvements over the previously reported 3D-VMM approach. Moreover, unlike the previous approach, the proposed VMM can be efficiently tailored to work in a smaller current output range. Our second major contribution is the development of 3D-aCortex, a multi-purpose neuromorphic inference processor that utilizes the proposed 3D-VMM b...
      Citation: Neuromorphic Computing and Engineering
      PubDate: 2021-07-15T23:00:00Z
      DOI: 10.1088/2634-4386/ac0775
      Issue No: Vol. 1, No. 1 (2021)
  • Comparing Loihi with a SpiNNaker 2 prototype on low-latency keyword
           spotting and adaptive robotic control

    • Authors: Yexin Yan; Terrence C Stewart, Xuan Choo, Bernhard Vogginger, Johannes Partzsch, Sebastian Höppner, Florian Kelber, Chris Eliasmith, Steve Furber Christian Mayr
      First page: 014002
      Abstract: We implemented two neural network based benchmark tasks on a prototype chip of the second-generation SpiNNaker (SpiNNaker 2) neuromorphic system: keyword spotting and adaptive robotic control. Keyword spotting is commonly used in smart speakers to listen for wake words, and adaptive control is used in robotic applications to adapt to unknown dynamics in an online fashion. We highlight the benefit of a multiply-accumulate (MAC) array in the SpiNNaker 2 prototype which is ordinarily used in rate-based machine learning networks when employed in a neuromorphic, spiking context. In addition, the same benchmark tasks have been implemented on the Loihi neuromorphic chip, giving a side-by-side comparison regarding power consumption and computation time. While Loihi shows better efficiency when less complicated vector-matrix multiplication is involved, with the MAC array, the SpiNNaker 2 prototype shows better efficiency when high dimensional vector-matrix multiplication is involved.
      Citation: Neuromorphic Computing and Engineering
      PubDate: 2021-07-15T23:00:00Z
      DOI: 10.1088/2634-4386/abf150
      Issue No: Vol. 1, No. 1 (2021)
  • Modularity and multitasking in neuro-memristive reservoir networks

    • Authors: Alon Loeffler; Ruomin Zhu, Joel Hochstetter, Adrian Diaz-Alvarez, Tomonobu Nakayama, James M Shine Zdenka Kuncic
      First page: 014003
      Abstract: The human brain seemingly effortlessly performs multiple concurrent and elaborate tasks in response to complex, dynamic sensory input from our environment. This capability has been attributed to the highly modular structure of the brain, enabling specific task assignment among different regions and limiting interference between them. Here, we compare the structure and functional capabilities of different bio-physically inspired and biological networks. We then focus on the influence of topological properties on the functional performance of highly modular, bio-physically inspired neuro-memristive nanowire networks (NWNs). We perform two benchmark reservoir computing tasks (memory capacity and nonlinear transformation) on simulated networks and show that while random networks outperform NWNs on independent tasks, NWNs with highly segregated modules achieve the best performance on simultaneous tasks. Conversely, networks that share too many resources, such as networks with random ...
      Citation: Neuromorphic Computing and Engineering
      PubDate: 2021-08-26T23:00:00Z
      DOI: 10.1088/2634-4386/ac156f
      Issue No: Vol. 1, No. 1 (2021)
  • Towards low loss non-volatile phase change materials in mid index

    • Authors: Joaquin Faneca; Ioannis Zeimpekis, S T Ilie, Thalía Domínguez Bucio, Katarzyna Grabska, Daniel W Hewak Frederic Y Gardes
      First page: 014004
      Abstract: Photonic integrated circuits currently use platform intrinsic thermo-optic and electro-optic effects to implement dynamic functions such as switching, modulation and other processing. Currently, there is a drive to implement field programmable photonic circuits, a need which is only magnified by new neuromorphic and quantum computing applications. The most promising non-volatile photonic components employ phase change materials such as GST and GSST, which had their origin in electronic memory. However, in the optical domain, these compounds introduce significant losses potentially preventing a large number of applications. Here, we evaluate the use of two newly introduced low loss phase change materials, Sb 2 S 3 and Sb 2 Se 3 , on a silicon nitride photonic platform for future implementation in neuromorphic computing. We focus the study on Mach–Zehnder interferometers that operate at the O and C bands to demonstrate the performance of the sy...
      Citation: Neuromorphic Computing and Engineering
      PubDate: 2021-08-26T23:00:00Z
      DOI: 10.1088/2634-4386/ac156e
      Issue No: Vol. 1, No. 1 (2021)
  • A spiking central pattern generator for the control of a simulated lamprey
           robot running on SpiNNaker and Loihi neuromorphic boards

    • Authors: Emmanouil Angelidis; Emanuel Buchholz, Jonathan Arreguit, Alexis Rougé, Terrence Stewart, Axel von Arnim, Alois Knoll Auke Ijspeert
      First page: 014005
      Abstract: Central pattern generator (CPG) models have long been used to investigate both the neural mechanisms that underlie animal locomotion, as well as for robotic research. In this work we propose a spiking central pattern generator (SCPG) neural network and its implementation on neuromorphic hardware as a means to control a simulated lamprey model. To construct our SCPG model, we employ the naturally emerging dynamical systems that arise through the use of recurrent neural populations in the neural engineering framework (NEF). We define the mathematical formulation behind our model, which consists of a system of coupled abstract oscillators modulated by high-level signals, capable of producing a variety of output gaits. We show that with this mathematical formulation of the CPG model, the model can be turned into a spiking neural network (SNN) that can be easily simulated with Nengo, an SNN simulator. The SCPG model is then used to produce the swimming gaits of a simulated lamprey ro...
      Citation: Neuromorphic Computing and Engineering
      PubDate: 2021-08-26T23:00:00Z
      DOI: 10.1088/2634-4386/ac1b76
      Issue No: Vol. 1, No. 1 (2021)
  • Hierarchical architectures in reservoir computing systems

    • Authors: John Moon; Yuting Wu Wei D Lu
      First page: 014006
      Abstract: Reservoir computing (RC) offers efficient temporal data processing with a low training cost by separating recurrent neural networks into a fixed network with recurrent connections and a trainable linear network. The quality of the fixed network, called reservoir, is the most important factor that determines the performance of the RC system. In this paper, we investigate the influence of the hierarchical reservoir structure on the properties of the reservoir and the performance of the RC system. Analogous to deep neural networks, stacking sub-reservoirs in series is an efficient way to enhance the nonlinearity of data transformation to high-dimensional space and expand the diversity of temporal information captured by the reservoir. These deep reservoir systems offer better performance when compared to simply increasing the size of the reservoir or the number of sub-reservoirs. Low frequency components are mainly captured by the sub-reservoirs in later stage of the deep reservoir...
      Citation: Neuromorphic Computing and Engineering
      PubDate: 2021-08-26T23:00:00Z
      DOI: 10.1088/2634-4386/ac1b75
      Issue No: Vol. 1, No. 1 (2021)
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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