Assessing Random Dynamical Network Architectures for Nanoelectronics

Independent of the technology, it is generally expected that future nanoscale devices will be built from vast numbers of densely arranged devices that exhibit high failure rates. Other than that, there is little consensus on what type of technology and computing architecture holds most promises to go far beyond today's top-down engineered silicon devices. Cellular automata (CA) have been proposed in the past as a possible class of architectures to the von Neumann computing architecture, which is not generally well suited for future parallel and fine-grained nanoscale electronics. While the top-down engineered semi-conducting technology favors regular and locally interconnected structures, future bottom-up self-assembled devices tend to have irregular structures because of the current lack precise control over these processes. In this paper, we will assess random dynamical networks, namely Random Boolean Networks (RBNs) and Random Threshold Networks (RTNs), as alternative computing architectures and models for future information processing devices. We will illustrate that--from a theoretical perspective--they offer superior properties over classical CA-based architectures, such as inherent robustness as the system scales up, more efficient information processing capabilities, and manufacturing benefits for bottom-up designed devices, which motivates this investigation. We will present recent results on the dynamic behavior and robustness of such random dynamical networks while also including manufacturing issues in the assessment.Comment: 8 pages, 6 figures, IEEE/ACM Symposium on Nanoscale Architectures, NANOARCH 2008, Anaheim, CA, USA, Jun 12-13, 200

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

Bio-inspired Hardware Architectures for Memory, Image Processing, and Control Applications

Emerging technologies are expected to partially replace and enhance CMOS systems as the end of transistor scaling approaches. A particular type of emerging technology of interest is the variable resistance devices due to their scalability, non-volatile nature, and CMOS process compatibility. The goal of this dissertation is to present circuit and system level applications of CMOS and variable resistance devices with bio-inspired computation paradigms as the main focus. The summary of the results offered per chapter is as follows: In the first chapter of this thesis, an introduction to the work presented in the rest of this thesis and the model for the variable resistance device is provided. In the second chapter of this thesis, a crossbar memory architecture that utilizes a reduced constraint read-monitored-write scheme is presented. Variable resistance based crossbar memories are prime candidates to succeed the Flash as the mainstream nonvolatile memory due to their density, scalability, and write endurance. The proposed scheme supports multi-bit storage per cell and utilizes reduced hardware, aiming to decrease the feedback complexity and latency while still operating with CMOS compatible voltages. Additionally, a read technique that can successfully distinguish resistive states under the existence of resistance drift due to read/write disturbances in the array is presented. Derivations of analytical relations are provided to set forth a design methodology in selecting peripheral device parameters. In the third chapter of this thesis, an analog programmable resistive grid-based architecture mimicking the cellular connections of a biological retina in the most basic level, capable of performing various real time image processing tasks such as edge and line detections, is presented. Resistive grid-based analog structures have been shown to have advantages of compact area, noise immunity, and lower power consumption compared to their digital counterparts. However, these are static structures that can only perform one type of image processing task. The proposed unit cell structure employs 3-D confined resonant tunneling diodes called quantum dots for signal amplification and latching, and these dots are interconnected between neighboring cells through non-volatile continuously variable resistive elements. A method to program connections is introduced and verified through circuit simulations. Various diffusion characteristics, edge detection, and line detection tasks have been demonstrated through simulations using a 2-D array of the proposed cell structure, and analytical models have been provided. In the fourth chapter of this thesis, a bio-inspired hardware designed to solve the optimal control problem for general systems is presented. Adaptive Dynamic Programming algorithms provide means to approximate optimal control actions for linear and non-linear systems. Action-Critic Networks based approach is an efficient way to approximately evaluate the cost function and the optimal control actions. However, due to its computation intensiveness, this approach is usually implemented in high level programming languages run using general purpose processors. The presented hardware design is aimed at reducing the computation time and the hardware overhead by using the Heuristic Dynamic Programming algorithm which is a form of Adaptive Dynamic Programming. The proposed hardware operating at mere speed of 10 MHz yields 237 times faster learning rate in comparison to conventional software implementations running on fast processors such as the 1.2 GHz Intel Xeon processor.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/136972/1/yalciny_1.pd

Study of neuromorphic properties of circuits based on resonant tunneling diodes

Tese de Mestrado Integrado, Engenharia Física, 2022, Universidade de Lisboa, Faculdade de CiênciasRTDs, with their negative differential conductance, small size, capability of high frequency of operation and excitable response have the potential to be implemented as basic components in spiking neuromorphic circuits, i.e as nodes that produce and detect spikes. The present study addresses the neuromorphic properties of resonant tunneling diodes (RTDs), namely their spike generation and detection dynamics. The layout consists firstly on the basic requirements for neuromorphic implementations, followed by a description of the physics behind the RTDs non-linear current voltage characteristic that creates the conditions for spike generation. The mechanisms and conditions by which the RTDs create an excitable response are then put-forward. After this, a series of experimental measurements were carried out for different sized RTDs provided by the ChipAi project. First the measurements of the RTDs I-V curves, followed by a characterisation on their operation as a VCO, concluded with a study on their excitable all-or-nothing response to electrical perturbations. Finally, several experimental activities were carried out to infer the properties and requirements of these devices to work as potential neuromorphic devices. These parameters were the voltage thresholds, resting potentials and refractory times. these were then discussed for all of the different sized RTDs and operation points. On top of this experimental study, tools of simulation were also designed and constructed for these RTDs devices followed by a verification study on not only on their VCO mode but also their neuromorphic properties i.e. excitable response. It was found that the RTDs can indeed function as spike generators when an appropriate disturbance is applied in their bias voltage. The prospect for the scaling down of these devices to work as nanoneuromorphic devices seems promising due to the fact that smaller RTDs presented higher frequencies of operation leading to shorter periods of oscillation and refractory times. The simulation tool, based on the RLC model of the RTDs, developed using Matlab Simulink/Simscape and the I-V curves of these devices, was able to emulate the oscillatory behaviour of the RTDs when biased in their NDC regions and the neuromorphic property of excitability, again it was found that the scaling down of these devices can lead to faster communication rates

Dynamics of resonant tunneling diode optoelectronic oscillators

Tese de dout., Física, Faculdade de Ciências e Tecnologia, Univ. do Algarve, 2012The nonlinear dynamics of optoelectronic integrated circuit (OEIC) oscillators comprising semiconductor resonant tunneling diode (RTD) nanoelectronic quantum devices has been investigated. The RTD devices used in this study oscillate in the microwave band frequency due to the negative di erential conductance (NDC) of their nonlinear current voltage characteristics, which is preserved in the optoelectronic circuit. The aim was to study RTD circuits incorporating laser diodes and photo-detectors to obtain novel dynamical operation regimes in both electrical and optical domains taking advantage of RTD's NDC characteristic. Experimental implementation and characterization of RTD-OEICs was realized in parallel with the development of computational numerical models. The numerical models were based on ordinary and delay di erential equations consisting of a Li enard's RTD oscillator and laser diode single mode rate equations that allowed the analysis of the dynamics of RTD-OEICs. In this work, several regimes of operation are demonstrated, both experimentally and numerically, including generation of voltage controlled microwave oscillations and synchronization to optical and electrical external signals providing stable and low phase noise output signals, and generation of complex oscillations that are characteristic of high-dimensional chaos. Optoelectronic integrated circuits using RTD oscillators are interesting alternatives for more e cient synchronization, generation of stable and low phase noise microwave signals, electrical/optical conversion, and for new ways of optoelectronic chaos generation. This can lead to simpli cation of communication systems by boosting circuits speed while reducing the power and number of components. The applications of RTD-OEICs include operation as optoelectronic voltage controlled oscillators in clock recovery circuit systems, in wireless-photonics communication systems, or in secure communication systems using chaotic waveforms

Multi-scale genetic network inference based on time series gene expression profiles

This work integrates multi-scale clustering and short-time correlation to estimate genetic networks with different time resolutions and detail levels. Gene expression data are noisy and large scale. Clustering is widely used to group genes with similar pattern. The cluster centers can be used to infer the genetic networks among these clusters. This work introduces the Multi-scale Fuzzy K-means clustering algorithm to uncover groups of coregulated genes and capture the networks in different levels of detail.;Time series expression profiles provide dynamic information for inferring gene regulatory relationships. Large scale network inference, identifying the transient interactions and feedback loops as well as differentiating direct and indirect interactions are among the major challenges of genetic network inference. Time correlation can estimate the time delay and edge direction. Partial correlation and directed-separation theory help differentiate direct and indirect interactions and identify feedback loops. This work introduces the constraint-based time-correlation (CBTC) network inference algorithm that combines these methods with time correlation estimation to more fully characterize genetic networks. Gene expression regulation can happen in specific time periods and conditions instead of across the whole expression profile. Short-time correlation can capture transient interactions.;The network discovery algorithm was mainly validated using yeast cell cycle data. The algorithm successfully identified the yeast cell cycle development stages, cell cycle and negative feedback loops, and indicated how the networks dynamically changes over time. The inferred networks reflect most interactions previously identified by genome-wide location analysis and match the extant literature. At detailed network level, the inferred networks provide more detailed information about genes (or clusters) and the interactions among them. Interesting genes, clusters and interactions were identified, which match the literature and the gene ontology information and provide hypotheses for further studies