A molecular neuromorphic network device consisting of single-walled carbon nanotubes complexed with polyoxometalate

In contrast to AI hardware, neuromorphic hardware is based on neuroscience, wherein constructing both spiking neurons and their dense and complex networks is essential to obtain intelligent abilities. However, the integration density of present neuromorphic devices is much less than that of human brains. In this report, we present molecular neuromorphic devices, composed of a dynamic and extremely dense network of single-walled carbon nanotubes (SWNTs) complexed with polyoxometalate (POM). We show experimentally that the SWNT/POM network generates spontaneous spikes and noise. We propose electron-cascading models of the network consisting of heterogeneous molecular junctions that yields results in good agreement with the experimental results. Rudimentary learning ability of the network is illustrated by introducing reservoir computing, which utilises spiking dynamics and a certain degree of network complexity. These results indicate the possibility that complex functional networks can be constructed using molecular devices, and contribute to the development of neuromorphic devices

STM-induced light emission from thin films of perylene derivatives on the HOPG and Au substrates

We have investigated the emission properties of N,N'-diheptyl-3,4,9,10-perylenetetracarboxylic diimide thin films by the tunneling-electron-induced light emission technique. A fluorescence peak with vibronic progressions with large Stokes shifts was observed on both highly ordered pyrolytic graphite (HOPG) and Au substrates, indicating that the emission was derived from the isolated-molecule-like film condition with sufficient π-π interaction of the perylene rings of perylenetetracarboxylic diimide molecules. The upconversion emission mechanism of the tunneling-electron-induced emission was discussed in terms of inelastic tunneling including multiexcitation processes. The wavelength-selective enhanced emission due to a localized tip-induced surface plasmon on the Au substrate was also obtained

Holmes : A Hardware-Oriented Optimizer Using Logarithms

Edge computing, which has been gaining attention in re-cent years, has many advantages, such as reducing the load on the cloud, not being affected by the communication environment, and providing excellent security. Therefore, many researchers have attempted to implement neural networks, which are representative of machine learning in edge computing. Neural networks can be divided into inference and learning parts; however, there has been little research on implementing the learning component in edge computing in contrast to the inference part. This is because learning requires more memory and computation than inference, easily exceeding the limit of resources available for edge computing. To overcome this prob-lem, this research focuses on the optimizer, which is the heart of learning. In this paper, we introduce our new optimizer, hardware-oriented logarith-mic momentum estimation (Holmes), which incorporates new perspectives not found in existing optimizers in terms of characteristics and strengths of hardware. The performance of Holmes was evaluated by comparing it with other optimizers with respect to learning progress and convergence speed. Important aspects of hardware implementation, such as memory and oper-ation requirements are also discussed. The results show that Holmes is a good match for edge computing with relatively low resource requirements and fast learning convergence. Holmes will help create an era in which advanced machine learning can be realized on edge computing

Noise sensitivity of physical reservoir computing in a ring array of atomic switches

Reservoir computing (RC) is possible using physical systems. We have previously proposed an RC for ideal atomic switches. When temporal current fluctuations (noise) from the measurement of actual atomic switches are introduced into the proposed RC, performance degrades significantly. To address this issue, we propose novel methods for increasing the operating current range and observing the atomic switch several times to determine the average noise. Consequently, the memory capacity of the RC model increased, despite the presence of noise. To improve the precision of RC, we investigated the capacity and showed that changing the time constant of atomic switches results in an improvement

Smart hardware architecture with random weight elimination and weight balancing algorithms

Reducing the number of connections in hardware artificial neural networks, as compared with their software counterparts, can result in a drastic reduction in costs, because the reduction translates into utilizing fewer devices. This paper presents the demonstration of a method, by using simulations, to halve the amount of weights in a network while minimizing the accuracy loss. Additionally, the appropriate considerations for translating these simulation results to hardware networks are also detailed

Digital implementation of a multilayer perception based on stochastic computing with learning function

Stochastic Computing (SC) [2] is a probability-based computing method, which enables the performance of various operations with a small number of logic gates (i.e., low power) in exchange for high accuracy. Using SC for edge artificial intelligence (AI) integrated circuits can help circumvent the limitations inherent in the power and area required for edge AI. In this study, a three-layered Neural Network (NN) is presented with an online learning function that introduces pseudo-activation, pseudo-subtraction, and imperfect addition into the SC framework. This method may expand the options for edge AI integrated circuits using SC

Heuristic model for configurable polymer wire synaptic devices

Recently, there has been considerable research on nonvolatile analog devices for artificial intelligence (AI); however, it focuses on all-coupled neural networks. In contrast, polymer wire-type synaptic devices, which can be expected to be arbitrarily wired similar to a biological neural network, have already been proposed and demonstrated. In this study, we model a polymer wire synaptic device based on the results of previous research, and demonstrate an example of applying simple perceptron (AI) to the model. The results of our study show that it is possible to predict effective methods of using polymer wire synaptic elements in AI

Simple Reservoir Computing Capitalizing on the Nonlinear Response of Materials: Theory and Physical Implementations

The potential of nonlinear dynamical systems serving as reservoirs has attracted much attention for the physical realization of reservoir computing (RC). Here, we propose a hardware system working as a reservoir with one simple form of nonlinearity that reflects the intrinsic characteristics of the materials. We show that insufficient dynamics in such physical systems can perform like complex dynamical systems with the assistance of external controls. Based on the idea of spatial multiplexing, this dynamical system is studied under two frameworks. The correlation between structural adjustments of the reservoir and system performance in processing various types of task is proposed. Our results are expected to enable the development of material-based devices for RC