Memristors

This Edited Volume Memristors - Circuits and Applications of Memristor Devices is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of Engineering. The book comprises single chapters authored by various researchers and edited by an expert active in the physical sciences, engineering, and technology research areas. All chapters are complete in itself but united under a common research study topic. This publication aims at providing a thorough overview of the latest research efforts by international authors on physical sciences, engineering, and technology,and open new possible research paths for further novel developments

Frontiers in Neuromorphic Engineering

Neurobiological processing systems are remarkable computational devices. They use slow, stochastic, and inhomogeneous computing elements and yet they outperform today’s most powerful computers at tasks such as vision, audition, and motor control, tasks that we perform nearly every moment that we are awake without much conscious thought or concern. Despite the vast amount of resources dedicated to the research and development of computing, information, and communication technologies, today’s fastest and largest computers are still not able to match biological systems at robustly accomplishing real-worl

Trends and challenges in neuroengineering: toward "Intelligent" neuroprostheses through brain-"brain inspired systems" communication

Future technologies aiming at restoring and enhancing organs function will intimately rely on near-physiological and energy-efficient communication between living and artificial biomimetic systems. Interfacing brain-inspired devices with the real brain is at the forefront of such emerging field, with the term "neurobiohybrids" indicating all those systems where such interaction is established. We argue that achieving a "high-level" communication and functional synergy between natural and artificial neuronal networks in vivo, will allow the development of a heterogeneous world of neurobiohybrids, which will include "living robots" but will also embrace “intelligent” neuroprostheses for augmentation of brain function. The societal and economical impact of intelligent neuroprostheses is likely to be potentially strong, as they will offer novel therapeutic perspectives for a number of diseases, and going beyond classical pharmaceutical schemes. However, they will unavoidably raise fundamental ethical questions on the intermingling between man and machine and more specifically, on how deeply it should be allowed that brain processing is affected by implanted "intelligent" artificial systems. Following this perspective, we provide the reader with insights on ongoing developments and trends in the field of neurobiohybrids. We address the topic also from a "community building" perspective, showing through a quantitative bibliographic analysis, how scientists working on the engineering of brain-inspired devices and brain-machine interfaces are increasing their interactions. We foresee that such trend preludes to a formidable technological and scientific revolution in brain-machine communication and to the opening of new avenues for restoring or even augmenting brain function for therapeutic purposes

Bio-inspired cellular machines:towards a new electronic paper architecture

Information technology has only been around for about fifty years. Although the beginnings of automatic calculation date from as early as the 17th century (W. Schickard built the first mechanical calculator in 1623), it took the invention of the transistor by W. Shockley, J. Bardeen and W. Brattain in 1947 to catapult calculators out of the laboratory and produce the omnipresence of information and communication systems in today's world. Computers not only boast very high performance, capable of carrying out billions of operations per second, they are taking over our world, working their way into every last corner of our environment. Microprocessors are in everything, from the quartz watch to the PC via the mobile phone, the television and the credit card. Their continuing spread is very probable, and they will even be able to get into our clothes and newspapers. The incessant search for increasingly powerful, robust and intelligent systems is not only based on the improvement of technologies for the manufacture of electronic chips, but also on finding new computer architectures. One important source of inspiration for the research of new architectures is the biological world. Nature is fascinating for an engineer: what could be more robust, intelligent and able to adapt and evolve than a living organism? Out of a simple cell, equipped with its own blueprint in the form of DNA, develops a complete multi-cellular organism. The characteristics of the natural world have often been studied and imitated in the design of adaptive, robust and fault-tolerant artificial systems. The POE model resumes the three major sources of bio-inspiration: the evolution of species (P: phylogeny), the development of a multi-cellular organism by division and differentiation (O: ontogeny) and learning by interaction with the environment (E: epigenesis). This thesis aims to contribute to the ontogenetic branch of the POE model, through the study of three completely original cellular machines for which the basic element respects the six following characteristics: it is (1) reconfigurable, (2) of minimal complexity, (3) present in large numbers, (4) interconnected locally with its neighboring elements, (5) equipped with a display capacity and (6) with sensor allowing minimal interaction. Our first realization, the BioWall, is made up of a surface of 4,000 basic elements or molecules, capable of creating all cellular systems with a maximum of 160 × 25 elements. The second realization, the BioCube, transposes the two-dimensional architecture of the BioWall into a two-dimensional space, limited to 4 × 4 × 4 = 64 basic elements or spheres. It prefigures a three-dimensional computer built using nanotechnologies. The third machine, named BioTissue, uses the same hypothesis as the BioWall while pushing its performance to the limits of current technical possibilities and offering the benefits of an autonomous system. The convergence of these three realizations, studied in the context of emerging technologies, has allowed us to propose and define the computer architecture of the future: the electronic paper

Hardware optimizations of dense binary hyperdimensional computing: Rematerialization of hypervectors, binarized bundling, and combinational associative memory

Brain-inspired hyperdimensional (HD) computing models neural activity patterns of the very size of the brain's circuits with points of a hyperdimensional space, that is, with hypervectors. Hypervectors are Ddimensional (pseudo)random vectors with independent and identically distributed (i.i.d.) components constituting ultra-wide holographic words: D = 10,000 bits, for instance. At its very core, HD computing manipulates a set of seed hypervectors to build composite hypervectors representing objects of interest. It demands memory optimizations with simple operations for an efficient hardware realization. In this article, we propose hardware techniques for optimizations of HD computing, in a synthesizable open-source VHDL library, to enable co-located implementation of both learning and classification tasks on only a small portion of Xilinx UltraScale FPGAs: (1)We propose simple logical operations to rematerialize the hypervectors on the fly rather than loading them from memory. These operations massively reduce the memory footprint by directly computing the composite hypervectors whose individual seed hypervectors do not need to be stored in memory. (2) Bundling a series of hypervectors over time requires a multibit counter per every hypervector component. We instead propose a binarized back-to-back bundling without requiring any counters. This truly enables onchip learning with minimal resources as every hypervector component remains binary over the course of training to avoid otherwise multibit components. (3) For every classification event, an associative memory is in charge of finding the closest match between a set of learned hypervectors and a query hypervector by using a distance metric. This operator is proportional to hypervector dimension (D), and hence may take O(D) cycles per classification event. Accordingly, we significantly improve the throughput of classification by proposing associative memories that steadily reduce the latency of classification to the extreme of a single cycle. (4) We perform a design space exploration incorporating the proposed techniques on FPGAs for a wearable biosignal processing application as a case study. Our techniques achieve up to 2.39 7 area saving, or 2,337 7 throughput improvement. The Pareto optimal HD architecture is mapped on only 18,340 configurable logic blocks (CLBs) to learn and classify five hand gestures using four electromyography sensors