1,720,961 research outputs found
Memristive tonotopic mapping with volatile resistive switching memory devices
Full text link: To reach the energy efficiency and the computing capability of biological neural networks, novel hardware systems and paradigms are required where the information needs to be processed in both spatial and temporal domains. Resistive switching memory (RRAM) devices appear as key enablers for the implementation of large-scale neuromorphic computing systems with high energy efficiency and extended scalability. Demonstrating a full set of spatiotemporal primitives with RRAM-based circuits remains an open challenge. By taking inspiration from the neurobiological processes in the human auditory systems, we develop neuromorphic circuits for memristive tonotopic mapping via volatile RRAM devices. Based on a generalized stochastic device-level approach, we demonstrate the main features of signal processing of cochlea, namely logarithmic integration and tonotopic mapping of signals. We also show that our tonotopic classification is suitable for speech recognition. These results support memristive devices for physical processing of temporal signals, thus paving the way for energy efficient, high density neuromorphic systems
Development of Crosspoint Memory Arrays for Neuromorphic Computing
Full text linkMemristor-based hardware accelerators play a crucial role in achieving energy-efficient big data processing and artificial intelligence, overcoming the limitations of traditional von Neumann architectures. Resistive-switching memories (RRAMs) combine a simple two-terminal structure with the possibility of tuning the device conductance. This Chapter revolves around the topic of emerging memristor-related technologies, starting from their fabrication, through the characterization of single devices up to the development of proof-of-concept experiments in the field of in-memory computing, hardware accelerators, and brain-inspired architecture. Non-volatile devices are optimized for large-size crossbars where the devices’ conductance encodes mathematical coefficients of matrices. By exploiting Kirchhoff’s and Ohm’s law the matrix–vector-multiplication between the conductance matrix and a voltage vector is computed in one step. Eigenvalues/eigenvectors are experimentally calculated according to the power-iteration algorithm, with a fast convergence within about 10 iterations to the correct solution and Principal Component Analysis of the Wine and Iris datasets, showing up to 98% accuracy comparable to a floating-point implementation. Volatile memories instead present a spontaneous change of device conductance with a unique similarity to biological neuron behavior. This characteristic is exploited to demonstrate a simple fully-memristive architecture of five volatile RRAMs able to learn, store, and distinguish up to 10 different items with a memory capability of a few seconds. The architecture is thus tested in terms of robustness under many experimental conditions and it is compared with the real brain, disclosing interesting mechanisms which resemble the biological brain
Seizure detection via reservoir computing in MoS2-based charge trap memory devices
Full text link: Neurological disorders are a substantial global health burden, affecting millions of people worldwide. A key challenge in developing effective treatments and preventive measures is the realization of low-power wearable systems with early detection capabilities. Traditional strategies rely on machine learning algorithms, but their computational demands often exceed what miniaturized systems can provide. Neuromorphic computing, inspired by the human brain, demonstrated capabilities of on-chip computing with low power consumption. In this context, bidimensional (2D) semiconductors hold notable promise, thanks to their unique electronic properties, atomic-scale thickness, and scalability, making them ideal for low-power applications. This work presents a neuromorphic reservoir computing system exploiting MoS2-based charge trap memories (CTMs) for processing of electrophysiological signals. Real-time seizures detection is achieved, thanks to the nonlinear integration of local-field potential (LFP) recorded from in vitro rodent models of ictogenesis. The results support MoS2-based CTMs for low-power biomedical devices in clinical diagnosis and treatment of epilepsy
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Emerging memory devices & systems for biologically plausible neuromorphic computing
No full textDOTTORATOOggigiorno, il volume dei dati prodotti nella nostra società sta crescendo esponenzialmente, così come la loro varietà e complessità. Automotive, smart cities e Industria 4.0 sono solo alcuni esempi di tendenze che stanno accelerando la domanda di capacità computazionali e di storage sempre maggiori. Superato il suo "secondo inverno", l'Intelligenza Artificiale (AI) offre oggi uno strumento per affrontare queste nuove sfide. Tuttavia, nelle sue implementazioni pratiche, l'AI tende a sovraccaricare le infrastrutture e i sistemi informatici, portando a un aumento ancora più drammatico della domanda computazionale. Il problema di fondo risiede nell'architettura dei computer moderni, proposta nel 1945 da Von Neumann. L'architettura di Von Neumann, dove l'unità di calcolo e di memoria sono separate, ci ha permesso di costruire macchine versatili e di uso generale negli ultimi 70 anni. Tuttavia, questa architettura sta ora mostrando significative inefficienze, principalmente a causa della necessità di spostare i dati tra le due unità. Questo problema è aggravato da un'asimmetria nel tasso di miglioramento tecnologico tra le unità di memoria e le unità di elaborazione e dall'avvicinarsi al limite fisico per il ridimensionamento della tecnologia CMOS. In questo contesto, emerge il Calcolo Neuromorfico: iniziato dal lavoro seminale di Carver Mead alla fine degli anni '80, questo nuovo paradigma si ispira alle strutture neurali biologiche per emulare il funzionamento e l'efficienza del cervello a livello hardware. Nel cervello, non vi è separazione tra calcolo e memoria: le due unità fondamentali, neuroni e sinapsi, lavorano in sinergia e i concetti di memoria e calcolo si fondono. Oltre a un cambiamento verso il calcolo in memoria, le reti biologiche sono caratterizzate dalla loro plasticità, ovvero la capacità di adattarsi continuamente agli stimoli. È la plasticità neuronale che è responsabile della nostra capacità di ricordare, apprendere e adattarsi attraverso una miriade di complessi meccanismi di plasticità che insieme risultano in efficienza energetica e capacità computazionali inimmaginabili nei loro controparti artificiali di oggi. Tuttavia, i nuovi paradigmi solitamente richiedono nuove tecnologie, ed è qui che entrano in gioco i dispositivi memristivi. Questi dispositivi di memoria emergenti non sono solo validi per supportare il futuro scaling tecnologico, ma possono potenzialmente implementare meccanismi che emulano la plasticità del cervello umano. Espandere e indagare questo insieme di meccanismi di plasticità e apprendimento è la sfida aperta del calcolo neuromorfico, portando a sistemi scalabili, efficienti e biologicamente plausibili. Questa tesi di dottorato si concentra sull'espansione dei meccanismi di plasticità ottenibili attraverso le proprietà dinamiche dei dispositivi memristivi e il loro utilizzo in sistemi neuromorfici biologicamente plausibili, dove quest'ultimo componente è cruciale per colmare il divario tra i modelli di neuroscienza computazionale e l'hardware per l'AI. L'approccio di questo lavoro si basa sul portare il calcolo all'interno del dispositivo esplorando le sue dinamiche intrinseche derivanti dalle sue proprietà fisiche. Viene presentato un quadro che definisce il confine tra memoria statica e dinamica nei sistemi neuromorfici e come questo impatti sull'emulazione dei meccanismi biologici. Questo si mappa su tre aree strutturali del lavoro, analoghe alle proprietà presenti nelle reti neurali biologiche: fattori esterni che modificano la plasticità, fattori dinamici interni che agiscono sulla plasticità e stocasticità.Nowadays, the volume of data produced in our society is exponentially surging, as is its
variety and complexity. Automotive, smart cities, and Industry 4.0 are just a few examples
of trends that are accelerating the demand for increased computational and storage
capacities. Having surpassed its "second winter", Artificial Intelligence (AI) today offers a
tool to address these new challenges. However, in its practical implementations, AI tends
to overload computing infrastructures and systems, leading to an even more dramatic
increase in computational demand. The underlying issue is rooted in the architecture
of modern computers, proposed in 1945 by Von Neumann. The Von Neumann architecture,
which separates the computing unit from the memory, has allowed us to build
flexible, general-purpose machines over the past 70 years. However, this architecture is
now showing significant inefficiencies primarily due to the need to move data between the
two units. This problem is exacerbated by an asymmetry in the rate of technological improvement
between memory units and processing units and by approaching the physical
limit for scaling CMOS technology. In this context, Neuromorphic Computing emerges:
started from the seminal work of Carver Mead in the late ’80s, this new paradigm draws
inspiration from biological neural structures to emulate the functioning and efficiency of
the brain at the hardware level. In the brain, there is no separation between computing
and memory: the two fundamental units, neurons and synapses, work in synergy, and the
concepts of memory and computing merge. Beyond a shift towards in-memory computing,
biological networks are characterized by their plasticity, or the ability to continuously
adapt to stimuli. It is neuronal plasticity that is responsible for our ability to remember,
learn, and adapt through a myriad of complex plasticity mechanisms that together result
in energy efficiency and computational capabilities that are unimaginable in their artificial
counterparts today. However, new paradigms usually require new technologies, and this
is where resistive switching devices come into play. These emerging memory devices are
not only viable for supporting future technological scaling but potentially can implement
mechanisms that emulate the plasticity of the human brain. Expanding and investigating
this set of plasticity and learning mechanisms is the open challenge of neuromorphic
computing, leading to scalable, efficient, and biologically plausible systems.
This doctoral thesis focuses on expanding the plasticity mechanisms achievable through
the dynamic properties of memristive devices and their use in biologically plausible neuromorphic
systems, where this latter component is crucial for bridging the gap between
models of computational neuroscience and hardware for AI. The approach of this work
is based on moving computation inside the device by exploring its intrinsic dynamics resulting
from its physical properties. A framework is presented that defines the boundary
between static and dynamic memory in neuromorphic systems and how this impacts the
emulation of biological mechanisms. This maps onto three structural areas of the work,
analogous to properties present in biological neural networks: external factors that modify
plasticity, internal dynamic factors that act on plasticity, and stochasticity.DIPARTIMENTO DI ELETTRONICA, INFORMAZIONE E BIOINGEGNERIAElectronics36FERRARI, GIORGIOPIRODDI, LUIG
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