Fabien Alibart

An Organic Nanoparticle Transistor Behaving as a Biological Spiking Synapse

Molecule-based devices are envisioned to complement silicon devices by providing new functions or by implementing existing functions at a simpler process level and lower cost, by virtue of their self-organization capabilities. Moreover, they are not bound to von Neuman architecture and this feature may open the way to other architectural paradigms. Neuromorphic electronics is one of them. Here, a device made of molecules and nanoparticles-a nanoparticle organic memory field-effect transistor (NOMFET)—that exhibits the main behavior of a biological spiking synapse is demonstrated. Facilitating and depressing synaptic behaviors can be reproduced by the NOMFET and can be programmed. The synaptic plasticity for real-time computing is evidenced and described by a simple model. These results open the way to rate-coding utilization of the NOMFET in dynamical neuromorphic computing circuits.

A Memristive Nanoparticle/Organic Hybrid Synapstor for Neuroinspired Computing

This work was funded by the European Union through the FP7 Project NABAB (Contract FP7-216777).

Plasticity in memristive devices for spiking neural networks.

Memristive devices present a new device technology allowing for the realization of compact non-volatile memories. Some of them are already in the process of industrialization. Additionally, they exhibit complex multilevel and plastic behaviors, which make them good candidates for the implementation of artificial synapses in neuromorphic engineering. However, memristive effects rely on diverse physical mechanisms, and their plastic behaviors differ strongly from one technology to another. Here, we present measurements performed on different memristive devices and the opportunities that they provide. We show that they can be used to implement different learning rules whose properties emerge directly from device physics: real time or accelerated operation, deterministic or stochastic behavior, long term or short term plasticity. We then discuss how such devices might be integrated into a complete architecture. These results highlight that there is no unique way to exploit memristive devices in neuromorphic systems. Understanding and embracing device physics is the key for their optimal use.

Pavlov's dog associative learning demonstrated on synaptic-like organic transistors

In this letter, we present an original demonstration of an associative learning neural network inspired by the famous Pavlovs dogs experiment. A single nanoparticle organic memory field effect transistor (NOMFET) is used to implement each synapse. We show how the physical properties of this dynamic memristive device can be used to perform low-power write operations for the learning and implement short-term association using temporal coding and spike-timing-dependent plasticity–based learning. An electronic circuit was built to validate the proposed learning scheme with packaged devices, with good reproducibility despite the complex synaptic-like dynamic of the NOMFET in pulse regime.

Functional Model of a Nanoparticle Organic Memory Transistor for Use as a Spiking Synapse

Emerging synapse-like nanoscale devices such as memristive devices and synaptic transistors are of great interest to provide adaptability, high density, and robustness for the development of new bio-inspired circuits or systems. We have recently reported the nanoparticle organic memory field-effect transistor (NOMFET), which exhibits behaviors similar to a biological spiking synapse in neural network. It is considered as a promising nanocomponent to design neuromorphic adaptive computing circuits and systems. A functional model of NOMFET is presented in this paper, which allows the reliable conception and simulation of hybrid nano/complimentary metal-oxide-semiconductor circuits and architectures. Spice simulations of the model have demonstrated good agreement with the experimental results. By using the model, some complex neuromorphic functions such as synaptic gain control have been simulated. The model is developed in Verilog-A language and implemented on Cadence Virtuoso platform with Spectre 5.1.41 simulator. An iterative physical model and a number of experimental parameters have been integrated to improve the simulation accuracy. Special techniques and methods for dynamic behavior modeling have been developed, which could be extended to other nanodevices.

Neuromorphic Computing Based on Emerging Memory Technologies

In this paper, we review some of the novel emerging memory technologies and how they can enable energy-efficient implementation of large neuromorphic computing systems. We will highlight some of the key aspects of biological computation that are being mimicked in these novel nanoscale devices, and discuss various strategies employed to implement them efficiently. Though large scale learning systems have not been implemented using these devices yet, we will discuss the ideal specifications and metrics to be satisfied by these devices based on theoretical estimations and simulations. We also outline the emerging trends and challenges in the path towards successful implementations of large learning systems that could be ubiquitously deployed for a wide variety of cognitive computing tasks.

Modeling and Experimental Demonstration of a Hopfield Network Analog-to-Digital Converter with Hybrid CMOS/Memristor Circuits

The purpose of this work was to demonstrate the feasibility of building recurrent artificial neural networks with hybrid complementary metal oxide semiconductor (CMOS)/memristor circuits. To do so, we modeled a Hopfield network implementing an analog-to-digital converter (ADC) with up to 8 bits of precision. Major shortcomings affecting the ADCs precision, such as the non-ideal behavior of CMOS circuitry and the specific limitations of memristors, were investigated and an effective solution was proposed, capitalizing on the in-field programmability of memristors. The theoretical work was validated experimentally by demonstrating the successful operation of a 4-bit ADC circuit implemented with discrete Pt/TiO2−x/Pt memristors and CMOS integrated circuit components.

OXRAM based ELM architecture for multi-class classification applications

In this paper, we show how metal-oxide (OxRAM) based nanoscale memory devices can be exploited to design low-power Extreme Learning Machine (ELM) architectures. In particular we fabricated HfO2 and TiO2 based OxRAM devices, and exploited their intrinsic resistance spread characteristics to realize ELM hidden layer weights and neuron biases. To validate our proposed OxRAM-ELM architecture, full-scale learning and multi-class classification simulations were performed for two complex datasets: (i) Land Satellite images and (ii) Image segmentation. Dependence of classification performance on neuron gain parameter and OxRAM device properties was studied in detail.

Neuromorphic hybrid RRAM-CMOS RBM architecture

Restricted Boltzmann Machines (RBMs) offer a key methodology to implement Deep Learning paradigms. This paper presents a novel approach for realizing a hybrid RRAM-CMOS RBM architecture. In our proposed hybrid RBM architecture, HfOx based (filamentary-type switching) RRAM devices are extensively used to implement: (i) Synapses (ii) Internal neuron-state storage and (iii) Stochastic neuron activation function. To validate the proposed scheme we simulated our RBM architecture for classification and reconstruction of hand-written digits on a reduced MNIST dataset of 6000 images. Contrastive-divergence (CD) specially optimized for RRAM devices was used to drive the synaptic weight update mechanism. Total required size of the RRAM matrix in the simulated application is of the order of ~ 0.4 Mb. Peak classification accuracy of 92 %, and an average accuracy of ~ 89 % was obtained over 100 training epochs. Average number of RRAM switching events was ~ 14 million/per epoch.

Exploiting the short-term to long-term plasticity transition in memristive nanodevice learning architectures

Memristive nanodevices offer new frontiers for computing systems that unite arithmetic and memory operations on-chip. Here, we explore the integration of electrochemical metallization cell (ECM) nanodevices with tunable filamentary switching in nanoscale learning systems. Such devices offer a natural transition between short-term plasticity (STP) and long-term plasticity (LTP). In this work, we show that this property can be exploited to efficiently solve noisy classification tasks. A single crossbar learning scheme is first introduced and evaluated. Perfect classification is possible only for simple input patterns, within critical timing parameters, and when device variability is weak. To overcome these limitations, a dual-crossbar learning system partly inspired by the extreme learning machine (ELM) approach is then introduced. This approach outperforms a conventional ELM-inspired system when the first layer is imprinted before training and testing, and especially so when variability in device timing evolution is considered: variability is therefore transformed from an issue to a feature. In attempting to classify the MNIST database under the same conditions, conventional ELM obtains 84% classification, the imprinted, uniform device system obtains 88% classification, and the imprinted, variable device system reaches 92% classification. We discuss benefits and drawbacks of both systems in terms of energy, complexity, area imprint, and speed. All these results highlight that tuning and exploiting intrinsic device timing parameters may be of central interest to future bio-inspired approximate computing systems.