Teses / dissertações sobre o tema "Neuro inspiré"
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Renaudo, Erwan. "Des comportements flexibles aux comportements habituels : meta-apprentissage neuro-inspiré pour la robotique autonome". Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066508/document.
Texto completo da fonteIn this work, we study how the notion of behavioral habit, inspired from the study of biology, can benefit to robots. Robot control architectures allow the robot to be able to plan to reach long term goals while staying reactive to events happening in the environment (Kortenkamp et Simmons, 2008). However, these architectures are rarely provided with learning capabilities that would allow them to acquire knowledge from experience. On the other hand, learning has been shown as an essential abiilty for behavioral adaptation in mammals. It permits flexible adaptation to new contexts but also efficient behavior in known contexts (Dickinson, 1985). The learning mechanisms are modeled as model-based (planning) and model-free (habitual) reinforcement learning algorithms (Sutton et Barto, 1998) which are combined into a global model of behavior (Daw et al., 2005). We proposed a robotic control architecture that take inspiration from this model of behavior and embed the two kinds of algorithms, and studied its performance in a robotic simulated task. None of the several methods for combining the algorithm we studied gave satisfying results, however, it allowed to identify some properties required for the planning process in a robotic task. We extended our study to two other tasks (one being on a real robot) and confirmed that combining the algorithms improves learning of the robot's behavior
Renaudo, Erwan. "Des comportements flexibles aux comportements habituels : meta-apprentissage neuro-inspiré pour la robotique autonome". Electronic Thesis or Diss., Paris 6, 2016. http://www.theses.fr/2016PA066508.
Texto completo da fonteIn this work, we study how the notion of behavioral habit, inspired from the study of biology, can benefit to robots. Robot control architectures allow the robot to be able to plan to reach long term goals while staying reactive to events happening in the environment (Kortenkamp et Simmons, 2008). However, these architectures are rarely provided with learning capabilities that would allow them to acquire knowledge from experience. On the other hand, learning has been shown as an essential abiilty for behavioral adaptation in mammals. It permits flexible adaptation to new contexts but also efficient behavior in known contexts (Dickinson, 1985). The learning mechanisms are modeled as model-based (planning) and model-free (habitual) reinforcement learning algorithms (Sutton et Barto, 1998) which are combined into a global model of behavior (Daw et al., 2005). We proposed a robotic control architecture that take inspiration from this model of behavior and embed the two kinds of algorithms, and studied its performance in a robotic simulated task. None of the several methods for combining the algorithm we studied gave satisfying results, however, it allowed to identify some properties required for the planning process in a robotic task. We extended our study to two other tasks (one being on a real robot) and confirmed that combining the algorithms improves learning of the robot's behavior
Cabaret, Théo. "Etude, réalisation et caractérisation de memristors organiques électro-greffés en tant que nanosynapses de circuits neuro-inspirés". Thesis, Paris 11, 2014. http://www.theses.fr/2014PA112168/document.
Texto completo da fonteThis PhD project takes place in the context of the study of neuromorphic circuits using memristor devices as synapses. The main objective is to evaluate a new class of organic memories developed at LICSEN (CEA Saclay/IRAMIS) and particularly their compatibility with the learning rules and the implementation strategy proposed by the Nanoarchi group at IEF (Univ. Paris-Sud, Orsay). These new memristors are based on the electro-grafting of organic redox complexes thin films to form robust and scalable metal/molecules/metal junctions. In addition to memristor fabrication, this work includes detailed electrical characterization studies (speed, retention property, scalability, robustness, etc.) aiming at, on the one hand, establishing the commutation mechanism in these new memristors and, on the other hand, evaluating their potential as synapses. This work also proposes a preparatory study of a neural-network type mixed-circuit demonstrator combining nano-memristors and conventional electronic (programmability of devices by spikes, fabrication of assemblies of memristors, variability). Moreover the demonstration of the compatibility of such memristors with the STDP (Spike Timing Dependent Plasticity) property and of the learning of a “conditioned reflex” opens the way to future unsupervised learning studies
Oliverio, Lucas. "Nonlinear dynamics from a laser diode with both optical injection and optical feedback for telecommunication applications". Electronic Thesis or Diss., CentraleSupélec, 2024. http://www.theses.fr/2024CSUP0002.
Texto completo da fonteThe current processing of information in large computing clusters is responsible for a strong energetic impact at a global level. The current paradigm needs to be rethought, and a computing architecture based on photonic components (semiconductor laser in particular) is studied in this thesis. The considered structure is a network of artificial neurons for telecommunications data processing. This involves using a laser diode to study the relationship between the dynamics with optical injection and optical feedback and neuroinspired computing capacity with simulations and experimental work
Williame, Jérôme. "Oscillateurs nanomagnétiques soumis à une boucle de rétroaction à retard : Bruit, chaos et applications neuromorphiques". Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS119.
Texto completo da fonteA delay feedback loop occurs when the output of a system is used to modify the input signal of the system. This phenomenon appears in fields as varied as the physics of amplifiers, the biology of insulin regulation or in social interactions. The effects of a delay feedback loop on an electronic system are well known and have given rise to many applications: phase-locked loops to improve stochastic properties, amplification or regulation loops, and so on. However, these feedback effects remain relatively unexplored in the context of nanomagnetic systems. In this thesis I have studied theoretically the consequences of delayed feedback on the magnetization dynamics of three different nanoscale systems with a separate focus for each system. The first involves spin-torque nano-oscillators whose stochastic properties and the impact of a feedback loop on them have been studied. It is found that significant changes can occur to the spectral linewidth, along with the appearance of secondary frequencies at large delays. The second system involves the macrospin oscillator, where I investigated how delayed feedback can induce chaotic transitions between the in-plane and out-ofplane precession states. These complex dynamics can be used to generate random numbers. The third system represents a proposal for implementing a Mackey-Glass oscillator using a domain wall racetrack-like geometry. By deforming this domain wall with spin polarized currents and with a suitable readout function, I show that this oscillator can be used for a time-delay architecture for reservoir computing. Tests of nonlinear time series prediction are conducted to evaluate the performance of this system
Chabi, Djaafar. "Architectures de circuits nanoélectroniques neuro-inspirée". Phd thesis, Université Paris Sud - Paris XI, 2012. http://tel.archives-ouvertes.fr/tel-00679300.
Texto completo da fonteVatin, Jeremy. "Photonique neuro-inspirée pour des applications télécoms". Electronic Thesis or Diss., CentraleSupélec, 2020. http://www.theses.fr/2020CSUP0004.
Texto completo da fonteWe are producing everyday thousands of gigabits of data, exchanged over the internet network. These data are processed thanks to computation clusters, which are responsible of the large amount of energy consumed by the internet network. In this work, we study an architecture made of photonic components, to get rid of electronic components that are power consuming. Thanks to components that are currently used in the internet network (laser and optical fiber), we aim at building an artificial neural network that is able to process telecommunication data. The artificial neural network is made of a laser, and an optical fiber that send back the light into the laser. The complex behavior of this system is used to feed the artificial neurons that are distributed along the fiber. We are able to prove that this system is able either to process one signal with a high efficiency, or two signals at the expense of a small loss of accuracy
Causo, Matteo. "Neuro-Inspired Energy-Efficient Computing Platforms". Thesis, Lille 1, 2017. http://www.theses.fr/2017LIL10004/document.
Texto completo da fonteBig Data highlights all the flaws of the conventional computing paradigm. Neuro-Inspired computing and other data-centric paradigms rather address Big Data to as resources to progress. In this dissertation, we adopt Hierarchical Temporal Memory (HTM) principles and theory as neuroscientific references and we elaborate on how Bayesian Machine Learning (BML) leads apparently totally different Neuro-Inspired approaches to unify and meet our main objectives: (i) simplifying and enhancing BML algorithms and (ii) approaching Neuro-Inspired computing with an Ultra-Low-Power prospective. In this way, we aim to bring intelligence close to data sources and to popularize BML over strictly constrained electronics such as portable, wearable and implantable devices. Nevertheless, BML algorithms demand for optimizations. In fact, their naïve HW implementation results neither effective nor feasible because of the required memory, computing power and overall complexity. We propose a less complex on-line, distributed nonparametric algorithm and show better results with respect to the state-of-the-art solutions. In fact, we gain two orders of magnitude in complexity reduction with only algorithm level considerations and manipulations. A further order of magnitude in complexity reduction results through traditional HW optimization techniques. In particular, we conceive a proof-of-concept on a FPGA platform for real-time stream analytics. Finally, we demonstrate we are able to summarize the ultimate findings in Machine Learning into a generally valid algorithm that can be implemented in HW and optimized for strictly constrained applications
Hirtzlin, Tifenn. "Digital Implementation of Neuromorphic systems using Emerging Memory devices". Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPAST071.
Texto completo da fonteWhile electronics has prospered inexorably for several decades, its leading source of progress will stop in the next coming years, due to the fundamental technological limits of transistors. Nevertheless, microelectronics is currently offering a major breakthrough: in recent years, memory technologies have undergone incredible progress, opening the way for multiple research venues in embedded systems. Additionally, a major feature for future years will be the ability to integrate different technologies on the same chip. new emerging memory devices that can be embedded in the core of the CMOS, such as Resistive Random Access Memory (RRAM) or Spin Torque Magnetic Tunnel Junction (STMRAM) based on naturally intelligent inmemory-computing architecture. Three braininspired algorithms are carefully examined: Bayesian reasoning binarized neural networks, and an approach that further exploits the intrinsic behavior of components, population coding of neurons. Each of these approaches explores different aspects of in-memory computing
Aboudib, Ala. "Neuro-inspired Architectures for the Acquisition and Processing of Visual Information". Thesis, Télécom Bretagne, 2016. http://www.theses.fr/2016TELB0419/document.
Texto completo da fonteComputer vision and machine learning are two hot research topics that have witnessed major breakthroughs in recent years. Much of the advances in these domains have been the fruits of many years of research on the visual cortex and brain function. In this thesis, we focus on designing neuro-inspired architectures for processing information along three different stages of the visual cortex. At the lowest stage, we propose a neural model for the acquisition of visual signals. This model is adapted to emulating eye movements and is closely inspired by the function and the architecture of the retina and early layers of the ventral stream. On the highest stage, we address the memory problem. We focus on an existing neuro-inspired associative memory model called the Sparse Clustered Network. We propose a new information retrieval algorithm that offers more flexibility and a better performance over existing ones. Furthermore, we suggest a generic formulation within which all existing retrieval algorithms can fit. It can also be used to guide the design of new retrieval approaches in a modular fashion. On the intermediate stage, we propose a new way for dealing with the image feature correspondence problem using a neural network model. This model deploys the structure of Sparse Clustered Networks, and offers a gain in matching performance over state-of-the-art, and provides a useful insight on how neuro-inspired architectures can serve as a substrate for implementing various vision tasks
Farquhar, Ethan David. "A biologically inspired silicon neuron". Thesis, Georgia Institute of Technology, 2003. http://hdl.handle.net/1853/14792.
Texto completo da fonteVincent, Adrien F. "Vers une utilisation synaptique de composants mémoires innovants pour l’électronique neuro-inspirée". Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS034/document.
Texto completo da fonteArtificial neural networks, which take some inspiration from the behavior of biological brains and their learning capabilities, are promising tools to address emerging computing uses known as “cognitive” tasks like classifying images or natural language interaction. However, implementing them on conventional computers is poorly efficient. A solution to this problem is to develop specialized acceleration chips which feature:• neurons, the information processing units, which can be implemented efficienctly with current electronic technologies;• synapses, the connections between the neurons which also support the learning process by adjusting their electrical conductance (“synaptic plasticity”). Implementing artificial synapses with high integration and on-line learning capabilities is still a challenge.This thesis explores the use of innovative memory nanodevices as artificial synapses: some of their rich plastic behaviors naturally implement features that are difficult to access with other devices.First, we investigate spin-transfer torque magnetic tunnel junctions, that are currently develop in industry as a new non volatile memory technology. We show that they can also be used as binary artificial synapses. After modeling their intrinsic stochastic behavior analytically, we describe how to harness this behavior to facilitate the implementation of an on-line probabilistic learning rule. With simulations tools developped in the laboratory, we detail the impact of the programming regime on the resilience of a system that uses such synapses, as well as on the system's power consumptionWe then investigate Ag2S electrochemical metalization cells, another type of innovative memory nanodevices fabricated and characterized by collaborators from Université de Lille I, who had already observed the existence of several plastic behaviors. We discovered an additional plasticity, close to a behavior known in neurosciences. With a simple analytical model that allows a better understanding of the relationships between theses plasticities, we show by simulations means a proof of concept of an unsupervised learning that relies on the interaction of the plastic behaviors theses nanodevices feature.Finally, we consider the challenges arising from the circuits that are required to read and write such artificial synapses in a neuro-inspired system.The results of this Ph.D. work pave the way for the design of neuro-inspired systems that can learn by harnessing the rich plastic behaviors that are featured by innovative memory nanodevices
Khan, Gul Muhammad. "Evolution of neuro-inspired Developmental Programs Capable of Learning". Thesis, University of York, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.490693.
Texto completo da fonteMokhtar, Maizura. "Bio-Inspired Autonomous Hardware Neuro-controller Device on an FPGA Inspired by the Hippocampus". Thesis, University of York, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.490697.
Texto completo da fontePINHO, ANDERSON GUIMARAES DE. "QUANTUM-INSPIRED EVOLUCIONARY ALGORITHM WITH MIXED REPRESENTATION APPLIED TO NEURO-EVOLUTION". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2010. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=17224@1.
Texto completo da fonteEsta dissertação objetivará a unificação de duas metodologias de algoritmos evolutivos consagradas para tratamento de problemas ou do tipo combinatórios, ou do tipo numéricos, num único algoritmo com representação mista. Trata-se de um algoritmo evolutivo inspirado na física quântica com representação mista binário-real do espaço de soluções, o AEIQ-BR. Este algoritmo trata-se de uma extensão do modelo com representação binária de Jang, Han e Kin, o AEIQ-B para otimizações combinatoriais, e o de representação real de Abs da Cruz, o AEIQ-R para otimizações numéricas. Com fins de exemplificação do novo algoritmo proposto, o discutiremos no contexto de neuroevolução, com o propósito de configurar completamente uma rede neural com alimentação adiante em termos: seleção de variáveis de entrada; números de neurônios na camada escondida; todos os pesos existentes; e tipos de funções de ativação de cada neurônio. Esta finalidade em se aplicar o algoritmo AEIQ-BR à neuroevolução – e também, numa analogia ao modelo NEIQ-R de Abs da Cruz – receberá a denominação NEIQ-BR. N de neuroevolução, E de evolutivo, IQ de inspiração quântica, e BR de binário-real. Para avaliar o desempenho do NEIQ-BR, utilizarse- á um total de seis casos benchmark de classificação, e outros dois casos reais, em campos da ciência como: finanças, biologia e química. Resultados serão comparados com algoritmos de outros pesquisadores e a modelagem manual de redes neurais, através de medidas de desempenho. Através de testes estatísticos concluiremos que o algoritmo NEIQ-BR apresentará um desempenho significativo na obtenção de previsões de classificação por neuroevolução.
This work aimed to unify two methodologies of evolutionary algorithms to treat problems with or combinatorial characteristics, or numeric, on a unique algorithm with mix representation. It is an evolutionary algorithm inspired in quantum physics with mixed representation of the solutions space, called QIEABR. This algorithm is an extension of the model with binary representation of the chromosome from Jang, Han e Kin, the QIEA-B for combinatorial optimization, and numeric representation from Abs da Cruz, the QIEA-R for numerical optimizations. For purposes of exemplification of the new algorithm, we will introduce the algorithm in the context of neuro-evolution, in order to completely configure a feed forward neural network in terms of: selection of input variables; numbers of neurons in the hidden layer; all existing synaptic weights; and types of activation functions of each neuron. This purpose when applying the algorithm QIEA-BR to neuro-evolution receive the designation of QIEN-BR. QI for quantum-inspired, E for evolutive, N for neuro-evolution, and BR for binary-real representation. To evaluate the performance of QIEN-BR, we will use a total of six benchmark cases of classification, and two real cases in fields of science such as finance, biology and chemistry. Results will be compared with algorithms of other researchers and manual modeling of neural networks through performance measures. Statistical tests will be provided to elucidate the significance of results, and what we can conclude is that the algorithm QIEN-BR better performance others researchers in terms of classification prediction.
Liu, Yang. "A neuro-immune inspired computational framework and its applications to a machine visual tracking system". Thesis, University of York, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.516625.
Texto completo da fonteAlecu, Lucian. "Une approche neuro-dynamique de conception des processus d'auto-organisation". Electronic Thesis or Diss., Nancy 1, 2011. http://www.theses.fr/2011NAN10031.
Texto completo da fonteIn this work we propose a cortically inspired neural architecture capable of developping an emergent process of self-organization. In order to implement this neural architecture in a distributed manner, we use the dynamic neural fields paradigm, a generic mathematical formalism aimed at modeling the competition between the neural activities at a mesoscopic level of the cortical structure. In order to examine in detail the dynamic properties of classical models, we design a formal criterion and an evaluation instrument, capable of analysing and quantifying the dynamic behavior of the any neural field, in specific contexts of stimulation. While this instrument highlights the practical advantages of the usage of such models, it also reveals the inability of these models to help implementing the self-organization process (implemented by the described architecture) with satisfactory results. These results lead us to suggest an alternative to the classical neural field models, based on a back-inhibition model which implements a local process of neural activity regulation. Thanks to this mechanism, the new neural field model is capable of achieving successful results in the implementation of the self-organization process described by our cortically inspired neural architecture. Moreover, a detailed analysis confirms that this new neural field maintains the features of the classical field models. The results described in this thesis open the perspectives for developping neuro-computational architectures for the design of software solutions or biologically-inspired robot applications
Alecu, Lucian. "Une approche neuro-dynamique de conception des processus d'auto-organisation". Phd thesis, Université Henri Poincaré - Nancy I, 2011. http://tel.archives-ouvertes.fr/tel-00606926.
Texto completo da fonteMaux, Ana Andr?a Barbosa. "Masculinidade a prova: um estudo de inspira??o fenomenologico - hermeneutico sobre a infertilidade masculina". Universidade Federal do Rio Grande do Norte, 2014. http://repositorio.ufrn.br:8080/jspui/handle/123456789/17403.
Texto completo da fonteCulturally, childbearing is understood as a situation that subjects will experience at some point in their lives, especially people who are married or have a similar affectionate relationship. Thus, to realize the inability to meet such a fate seems to be a natural cultural trigger of suffering, frustration and feelings of inadequacy and helplessness. Specifically for men, infertility is closely related to loss of masculinity, virility. He fails in his role as a male. This study sought to understand the impact that infertility have on the existence of a man who receives such a diagnosis, both in self-image as in their marital, sexual and professional roles. This study sets up as a hermeneutic phenomenological research based on the ideas of the philosopher Martin Heidegger. Participants were seven heterosexual, married and infertile men. Two interviews were conducted. The analysis of the material included both the material of the narratives, as the affectation of the researcher when interacting with the participants and their narratives, through phenomenological-hermeneutic interpretation. The results corroborate the literature that states the difficulty of the men, immersed in a context that defines them as virile, powerful and invulnerable to worry about issues related to health and disease. The possibility of any condition that impairs the reproductive capacity exceeds the acceptable limits of daily life for these men, not being recognized as a model of masculinity present in the condition in which they recognize. This leads to questions about their masculinity, role in the marital relationship and their existence. Thus, to recognize themselves as infertile surpass a medical diagnosis and is associated with the construction of meaning for their existence from the approximation with the infertility condition, which helps in redirecting their choices, restoring the project to be self and allowing further recognition as men. In the marital relationship, doing what they can to ensure, theirs happiness. Through these actions, they remain playing the role of family provider, showing that they are able to protect their wives and taking in assisted reproduction or adoption of children viable alternatives to fulfill the desire to leave a legacy and give a child to their wives and to society. Another result observed, refers to the ontological condition of care that characterizes the human being. The ways in which men are treated socially demonstrates a type of care that focuses on the development of characteristics such as strength, virility and determination but does not allow them to cope with the suffering of emotionally difficult situations, such as the diagnosis of infertility. At the end, the study gives rise to reflections on the need to provide a 12 space for men and their expressions of suffering, as well as to recognize their ability to overcome the painful and difficult situations
Culturalmente, procriar ? compreendido como uma situa??o que o sujeito deve vivenciar em algum momento de sua vida. Descobrir-se incapaz de cumprir tal destino naturalizado parece ser desencadeador de sofrimento, frustra??o e sentimentos de incapacidade e impot?ncia. Especificamente para o homem, a infertilidade est? estreitamente relacionada ? perda de masculinidade, de virilidade. Ele fracassa em seu papel de macho . O presente estudo buscou compreender os impactos que a infertilidade produz na exist?ncia do homem que recebe tal diagn?stico, tanto nos modos de perceber a si mesmo quanto no papel conjugal, sexual e profissional. Configura-se como uma pesquisa de inspira??o fenomenol?gico-hermen?utica, baseada em ideias do fil?sofo Martin Heidegger. Participaram do estudo sete homens heterossexuais, casados e com diagn?stico de infertilidade, sendo realizadas duas entrevistas individuais. A an?lise do material compreendeu tanto o material das narrativas quanto as afeta??es da pesquisadora por ocasi?o do contato com os colaboradores e suas narrativas, por meio da interpreta??o fenomenol?gico-hermen?utica. Os resultados corroboram dados da literatura, os quais afirmam a dificuldade dos homens, imersos em um contexto que exige que eles sejam viris, potentes e invulner?veis, de se preocuparem com as quest?es relacionadas ? sa?de e ? doen?a. Assim, a possibilidade de alguma condi??o que dificulte a sua capacidade reprodutiva, ultrapassa os limites aceit?veis no horizonte cotidiano de sentido que comp?e as vidas destes homens, n?o sendo reconhecida como uma condi??o presente no modelo de masculinidade no qual eles se reconhecem. Isto leva a um questionamento a respeito de sua masculinidade, de seu papel na rela??o conjugal e na pr?pria exist?ncia. Reconhecerem-se na condi??o de homens inf?rteis vai al?m de um diagn?stico m?dico e est? relacionado ? constru??o de novos sentidos existenciais. A partir da aproxima??o com tal condi??o, redirecionam suas escolhas, resgatando o projeto de ser si-mesmos e possibilitando continuarem se reconhecendo homens. Na rela??o conjugal, fazem o que estiver ao seu alcance para garantir, de alguma forma, que as esposas se sintam satisfeitas e, atrav?s destas a??es, eles permanecem exercendo o papel de provedores da fam?lia, demonstrando serem capazes de ampar?-las e proteg?-las, tendo, na reprodu??o assistida ou da ado??o de uma crian?a, alternativas vi?veis para concretizar o desejo de deixar um legado e de prover ? fam?lia e a sociedade. Outro resultado observado diz respeito ? condi??o ontol?gica de solicitude que caracteriza o ser humano. V?rias formas como os homens s?o 10 tratados pelas pessoas que est?o mais pr?ximas demonstram um tipo de solicitude no qual, ao mesmo tempo em que s?o valorizadas caracter?sticas como for?a, virilidade e determina??o, n?o ? permitido que os homens se aproximem de sofrimento, poupando-os de situa??es emocionalmente dif?ceis, como ? o caso do diagn?stico de infertilidade. Ao final, o estudo enseja reflex?es sobre a necessidade de proporcionar espa?os de acolhimento e de escuta aos homens e ?s suas express?es de sofrimento, bem como o reconhecimento de sua capacidade de supera??o das situa??es dolorosas e dif?ceis
Dai, Jing. "Reservoir-computing-based, biologically inspired artificial neural networks and their applications in power systems". Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47646.
Texto completo da fonte(6838184), Parami Wijesinghe. "Neuro-inspired computing enhanced by scalable algorithms and physics of emerging nanoscale resistive devices". 2019.
Encontre o texto completo da fonteDeep ‘Analog Artificial Neural Networks’ (AANNs) perform complex classification problems with high accuracy. However, they rely on humongous amount of power to perform the calculations, veiling the accuracy benefits. The biological brain on the other hand is significantly more powerful than such networks and consumes orders of magnitude less power, indicating some conceptual mismatch. Given that the biological neurons are locally connected, communicate using energy efficient trains of spikes, and the behavior is non-deterministic, incorporating these effects in Artificial Neural Networks (ANNs) may drive us few steps towards a more realistic neural networks.
Emerging devices can offer a plethora of benefits including power efficiency, faster operation, low area in a vast array of applications. For example, memristors and Magnetic Tunnel Junctions (MTJs) are suitable for high density, non-volatile Random Access Memories when compared with CMOS implementations. In this work, we analyze the possibility of harnessing the characteristics of such emerging devices, to achieve neuro-inspired solutions to intricate problems.
We propose how the inherent stochasticity of nano-scale resistive devices can be utilized to realize the functionality of spiking neurons and synapses that can be incorporated in deep stochastic Spiking Neural Networks (SNN) for image classification problems. While ANNs mainly dwell in the aforementioned classification problem solving domain, they can be adapted for a variety of other applications. One such neuro-inspired solution is the Cellular Neural Network (CNN) based Boolean satisfiability solver. Boolean satisfiability (k-SAT) is an NP-complete (k≥3) problem that constitute one of the hardest classes of constraint satisfaction problems. We provide a proof of concept hardware based analog k-SAT solver that is built using MTJs. The inherent physics of MTJs, enhanced by device level modifications, is harnessed here to emulate the intricate dynamics of an analog, CNN based, satisfiability (SAT) solver.
Furthermore, in the effort of reaching human level performance in terms of accuracy, increasing the complexity and size of ANNs is crucial. Efficient algorithms for evaluating neural network performance is of significant importance to improve the scalability of networks, in addition to designing hardware accelerators. We propose a scalable approach for evaluating Liquid State Machines: a bio-inspired computing model where the inputs are sparsely connected to a randomly interlinked reservoir (or liquid). It has been shown that biological neurons are more likely to be connected to other neurons in the close proximity, and tend to be disconnected as the neurons are spatially far apart. Inspired by this, we propose a group of locally connected neuron reservoirs, or an ensemble of liquids approach, for LSMs. We analyze how the segmentation of a single large liquid to create an ensemble of multiple smaller liquids affects the latency and accuracy of an LSM. In our analysis, we quantify the ability of the proposed ensemble approach to provide an improved representation of the input using the Separation Property (SP) and Approximation Property (AP). Our results illustrate that the ensemble approach enhances class discrimination (quantified as the ratio between the SP and AP), leading to improved accuracy in speech and image recognition tasks, when compared to a single large liquid. Furthermore, we obtain performance benefits in terms of improved inference time and reduced memory requirements, due to lower number of connections and the freedom to parallelize the liquid evaluation process.