Optoelectronic Reservoir Computing

Reservoir computing is a recently introduced, highly efficient bio-inspired approach for processing time dependent data. The basic scheme of reservoir computing consists of a non linear recurrent dynamical system coupled to a single input layer and a single output layer. Within these constraints many implementations are possible. Here we report an opto-electronic implementation of reservoir computing based on a recently proposed architecture consisting of a single non linear node and a delay line. Our implementation is sufficiently fast for real time information processing. We illustrate its performance on tasks of practical importance such as nonlinear channel equalization and speech recognition, and obtain results comparable to state of the art digital implementations.Comment: Contains main paper and two Supplementary Material

Reservoir Computing in Materio : An Evaluation of Configuration through Evolution

Recent work has shown that computational substrates made from carbon nanotube/polymer mixtures can form trainable Reservoir Computers. This new reservoir computing platform uses computer based evolutionary algorithms to optimise a set of electrical control signals to induce reservoir properties within the substrate. In the training process, evolution decides the value of analogue control signals (voltages) and the location of inputs and outputs on the substrate that improve the performance of the subsequently trained reservoir readout. Here, we evaluate the performance of evolutionary search compared to randomly assigned electrical configurations. The substrate is trained and evaluated on time-series prediction using the Santa Fe Laser generated competition data (dataset A). In addition to the main investigation, we introduce two new features closely linked to the traditional reservoir computing architecture, adding an evolvable input-weighting mechanism and a reservoir time-scaling parameter. The experimental results show evolved configurations across all four test substrates consistently produce reservoirs with greater performance than randomly configured reservoirs. The results also show that applying both input-weighting and timescaling simultaneously can provide additional tuning to the task, improving performance. For one material, the evolved reservoir is shown to outperform – for this task – all other hardwarebased reservoir computers found in the literature. The same material also outperforms a simple evolved simulated Echo State Network of the same size. The performance of this material is reported to be both consistent after long time-periods and after reconfiguration to other tasks

Gaussian states provide universal and versatile quantum reservoir computing

We establish the potential of continuous-variable Gaussian states in performing reservoir computing with linear dynamical systems in classical and quantum regimes. Reservoir computing is a machine learning approach to time series processing. It exploits the computational power, high-dimensional state space and memory of generic complex systems to achieve its goal, giving it considerable engineering freedom compared to conventional computing or recurrent neural networks. We prove that universal reservoir computing can be achieved without nonlinear terms in the Hamiltonian or non-Gaussian resources. We find that encoding the input time series into Gaussian states is both a source and a means to tune the nonlinearity of the overall input-output map. We further show that reservoir computing can in principle be powered by quantum fluctuations, such as squeezed vacuum, instead of classical intense fields. Our results introduce a new research paradigm for quantum reservoir computing and the engineering of Gaussian quantum states, pushing both fields into a new direction.Comment: 13 pages, 4 figure

Organic electrochemical networks for biocompatible and implantable machine learning: Organic bioelectronic beyond sensing

How can the brain be such a good computer? Part of the answer lies in the astonishing number of neurons and synapses that process electrical impulses in parallel. Part of it must be found in the ability of the nervous system to evolve in response to external stimuli and grow, sharpen, and depress synaptic connections. However, we are far from understanding even the basic mechanisms that allow us to think, be aware, recognize patterns, and imagine. The brain can do all this while consuming only around 20 Watts, out-competing any human-made processor in terms of energy-efficiency. This question is of particular interest in a historical era and technological stage where phrases like machine learning and artificial intelligence are more and more widespread, thanks to recent advances produced in the field of computer science. However, brain-inspired computation is today still relying on algorithms that run on traditional silicon-made, digital processors. Instead, the making of brain-like hardware, where the substrate itself can be used for computation and it can dynamically update its electrical pathways, is still challenging. In this work, I tried to employ organic semiconductors that work in electrolytic solutions, called organic mixed ionic-electronic conductors (OMIECs) to build hardware capable of computation. Moreover, by exploiting an electropolymerization technique, I could form conducting connections in response to electrical spikes, in analogy to how synapses evolve when the neuron fires. After demonstrating artificial synapses as a potential building block for neuromorphic chips, I shifted my attention to the implementation of such synapses in fully operational networks. In doing so, I borrowed the mathematical framework of a machine learning approach known as reservoir computing, which allows computation with random (neural) networks. I capitalized my work on demonstrating the possibility of using such networks in-vivo for the recognition and classification of dangerous and healthy heartbeats. This is the first demonstration of machine learning carried out in a biological environment with a biocompatible substrate. The implications of this technology are straightforward: a constant monitoring of biological signals and fluids accompanied by an active recognition of the presence of malign patterns may lead to a timely, targeted and early diagnosis of potentially mortal conditions. Finally, in the attempt to simulate the random neural networks, I faced difficulties in the modeling of the devices with the state-of-the-art approach. Therefore, I tried to explore a new way to describe OMIECs and OMIECs-based devices, starting from thermodynamic axioms. The results of this model shine a light on the mechanism behind the operation of the organic electrochemical transistors, revealing the importance of the entropy of mixing and suggesting new pathways for device optimization for targeted applications

Brain-inspired nanophotonic spike computing:challenges and prospects

Nanophotonic spiking neural networks (SNNs) based on neuron-like excitable subwavelength (submicrometre) devices are of key importance for realizing brain-inspired, power-efficient artificial intelligence (AI) systems with high degree of parallelism and energy efficiency. Despite significant advances in neuromorphic photonics, compact and efficient nanophotonic elements for spiking signal emission and detection, as required for spike-based computation, remain largely unexplored. In this invited perspective, we outline the main challenges, early achievements, and opportunities toward a key-enabling photonic neuro-architecture using III-V/Si integrated spiking nodes based on nanoscale resonant tunnelling diodes (nanoRTDs) with folded negative differential resistance. We utilize nanoRTDs as nonlinear artificial neurons capable of spiking at high-speeds. We discuss the prospects for monolithic integration of nanoRTDs with nanoscale light-emitting diodes and nanolaser diodes, and nanophotodetectors to realize neuron emitter and receiver spiking nodes, respectively. Such layout would have a small footprint, fast operation, and low power consumption, all key requirements for efficient nano-optoelectronic spiking operation. We discuss how silicon photonics interconnects, integrated photorefractive interconnects, and 3D waveguide polymeric interconnections can be used for interconnecting the emitter-receiver spiking photonic neural nodes. Finally, using numerical simulations of artificial neuron models, we present spike-based spatio-temporal learning methods for applications in relevant AI-based functional tasks, such as image pattern recognition, edge detection, and SNNs for inference and learning. Future developments in neuromorphic spiking photonic nanocircuits, as outlined here, will significantly boost the processing and transmission capabilities of next-generation nanophotonic spike-based neuromorphic architectures for energy-efficient AI applications. This perspective paper is a result of the European Union funded research project ChipAI in the frame of the Horizon 2020 Future and Emerging Technologies Open programme.</p

Neuromorphic nanophotonic systems for artificial intelligence

Over the last decade, we have witnessed an astonishing pace of development in the field of artificial intelligence (AI), followed by proliferation of AI algorithms into virtually every domain of our society. While modern AI models boast impressive performance, they also require massive amounts of energy and resources for operation. This is further fuelling the research into AI-specific, optimised computing hardware. At the same time, the remarkable energy efficiency of the brain brings an interesting question: Can we further borrow from the working principles of biological intelligence to realise a more efficient artificial intelligence? This can be considered as the main research question in the field of neuromorphic engineering. Thanks to the developments in AI and recent advancements in the field of photonics and photonic integration, research into light-powered implementations of neuromorphic hardware has recently experienced a significant uptick of interest. In such hardware, the aim is to seize some of the highly desirable properties of photonics not just for communication, but also to perform computation. Neurons in the brain frequently process information (compute) and communicate using action potentials, which are brief voltage spikes that encode information in the temporal domain. Similar dynamical behaviour can be elicited in some photonic devices, at speeds multiple orders of magnitude higher. Such devices with the capability of neuron-like spiking are of significant research interest for the field of neuromorphic photonics. Two distinct types of such excitable, spiking systems operating with optical signals are studied and investigated in this thesis. First, a vertical cavity surface emitting laser (VCSEL) can be operated under a specific set of conditions to realise a high-speed, all-optical excitable photonic neuron that operates at standard telecom wavelengths. The photonic VCSEL-neuron was dynamically characterised and various information encoding mechanisms were studied in this device. In particular, a spiking rate-coding regime of operation was experimentally demonstrated, and its viability for performing spiking domain conversion of digital images was explored. Furthermore, for the first time, a joint architecture utilising a VCSEL-neuron coupled to a photonic integrated circuit (PIC) silicon microring weight bank was experimentally demonstrated in two different functional layouts. Second, an optoelectronic (O/E/O) circuit based upon a resonant tunnelling diode (RTD) was introduced. Two different types of RTD devices were studied experimentally: a higher output power, µ-scale RTD that was RF coupled to an active photodetector and a VCSEL (this layout is referred to as a PRL node); and a simplified, photosensitive RTD with nanoscale injector that was RF coupled to a VCSEL (referred to as a nanopRL node). Hallmark excitable behaviours were studied in both devices, including excitability thresholding and refractory periods. Furthermore, a more exotic resonate and-fire dynamical behaviour was also reported in the nano-pRL device. Finally, a modular numerical model of the RTD was introduced, and various information processing methods were demonstrated using both a single RTD spiking node, as well as a perceptron-type spiking neural network with physical models of optoelectronic RTD nodes serving as artificial spiking neurons.Over the last decade, we have witnessed an astonishing pace of development in the field of artificial intelligence (AI), followed by proliferation of AI algorithms into virtually every domain of our society. While modern AI models boast impressive performance, they also require massive amounts of energy and resources for operation. This is further fuelling the research into AI-specific, optimised computing hardware. At the same time, the remarkable energy efficiency of the brain brings an interesting question: Can we further borrow from the working principles of biological intelligence to realise a more efficient artificial intelligence? This can be considered as the main research question in the field of neuromorphic engineering. Thanks to the developments in AI and recent advancements in the field of photonics and photonic integration, research into light-powered implementations of neuromorphic hardware has recently experienced a significant uptick of interest. In such hardware, the aim is to seize some of the highly desirable properties of photonics not just for communication, but also to perform computation. Neurons in the brain frequently process information (compute) and communicate using action potentials, which are brief voltage spikes that encode information in the temporal domain. Similar dynamical behaviour can be elicited in some photonic devices, at speeds multiple orders of magnitude higher. Such devices with the capability of neuron-like spiking are of significant research interest for the field of neuromorphic photonics. Two distinct types of such excitable, spiking systems operating with optical signals are studied and investigated in this thesis. First, a vertical cavity surface emitting laser (VCSEL) can be operated under a specific set of conditions to realise a high-speed, all-optical excitable photonic neuron that operates at standard telecom wavelengths. The photonic VCSEL-neuron was dynamically characterised and various information encoding mechanisms were studied in this device. In particular, a spiking rate-coding regime of operation was experimentally demonstrated, and its viability for performing spiking domain conversion of digital images was explored. Furthermore, for the first time, a joint architecture utilising a VCSEL-neuron coupled to a photonic integrated circuit (PIC) silicon microring weight bank was experimentally demonstrated in two different functional layouts. Second, an optoelectronic (O/E/O) circuit based upon a resonant tunnelling diode (RTD) was introduced. Two different types of RTD devices were studied experimentally: a higher output power, µ-scale RTD that was RF coupled to an active photodetector and a VCSEL (this layout is referred to as a PRL node); and a simplified, photosensitive RTD with nanoscale injector that was RF coupled to a VCSEL (referred to as a nanopRL node). Hallmark excitable behaviours were studied in both devices, including excitability thresholding and refractory periods. Furthermore, a more exotic resonate and-fire dynamical behaviour was also reported in the nano-pRL device. Finally, a modular numerical model of the RTD was introduced, and various information processing methods were demonstrated using both a single RTD spiking node, as well as a perceptron-type spiking neural network with physical models of optoelectronic RTD nodes serving as artificial spiking neurons