14 research outputs found
A Compact CMOS Memristor Emulator Circuit and its Applications
Conceptual memristors have recently gathered wider interest due to their
diverse application in non-von Neumann computing, machine learning,
neuromorphic computing, and chaotic circuits. We introduce a compact CMOS
circuit that emulates idealized memristor characteristics and can bridge the
gap between concepts to chip-scale realization by transcending device
challenges. The CMOS memristor circuit embodies a two-terminal variable
resistor whose resistance is controlled by the voltage applied across its
terminals. The memristor 'state' is held in a capacitor that controls the
resistor value. This work presents the design and simulation of the memristor
emulation circuit, and applies it to a memcomputing application of maze solving
using analog parallelism. Furthermore, the memristor emulator circuit can be
designed and fabricated using standard commercial CMOS technologies and opens
doors to interesting applications in neuromorphic and machine learning
circuits.Comment: Submitted to International Symposium of Circuits and Systems (ISCAS)
201
Can biological quantum networks solve NP-hard problems?
There is a widespread view that the human brain is so complex that it cannot
be efficiently simulated by universal Turing machines. During the last decades
the question has therefore been raised whether we need to consider quantum
effects to explain the imagined cognitive power of a conscious mind.
This paper presents a personal view of several fields of philosophy and
computational neurobiology in an attempt to suggest a realistic picture of how
the brain might work as a basis for perception, consciousness and cognition.
The purpose is to be able to identify and evaluate instances where quantum
effects might play a significant role in cognitive processes.
Not surprisingly, the conclusion is that quantum-enhanced cognition and
intelligence are very unlikely to be found in biological brains. Quantum
effects may certainly influence the functionality of various components and
signalling pathways at the molecular level in the brain network, like ion
ports, synapses, sensors, and enzymes. This might evidently influence the
functionality of some nodes and perhaps even the overall intelligence of the
brain network, but hardly give it any dramatically enhanced functionality. So,
the conclusion is that biological quantum networks can only approximately solve
small instances of NP-hard problems.
On the other hand, artificial intelligence and machine learning implemented
in complex dynamical systems based on genuine quantum networks can certainly be
expected to show enhanced performance and quantum advantage compared with
classical networks. Nevertheless, even quantum networks can only be expected to
efficiently solve NP-hard problems approximately. In the end it is a question
of precision - Nature is approximate.Comment: 38 page
From Quantum Materials to Microsystems
: The expression "quantum materials" identifies materials whose properties "cannot be described in terms of semiclassical particles and low-level quantum mechanics", i.e., where lattice, charge, spin and orbital degrees of freedom are strongly intertwined. Despite their intriguing and exotic properties, overall, they appear far away from the world of microsystems, i.e., micro-nano integrated devices, including electronic, optical, mechanical and biological components. With reference to ferroics, i.e., functional materials with ferromagnetic and/or ferroelectric order, possibly coupled to other degrees of freedom (such as lattice deformations and atomic distortions), here we address a fundamental question: "how can we bridge the gap between fundamental academic research focused on quantum materials and microsystems?". Starting from the successful story of semiconductors, the aim of this paper is to design a roadmap towards the development of a novel technology platform for unconventional computing based on ferroic quantum materials. By describing the paradigmatic case of GeTe, the father compound of a new class of materials (ferroelectric Rashba semiconductors), we outline how an efficient integration among academic sectors and with industry, through a research pipeline going from microscopic modeling to device applications, can bring curiosity-driven discoveries to the level of CMOS compatible technology
Avalanches and the edge-of-chaos in neuromorphic nanowire networks
The brain's efficient information processing is enabled by the interplay between its neuro-synaptic elements and complex network structure. This work reports on the neuromorphic dynamics of nanowire networks (NWNs), a brain-inspired system with synapse-like memristive junctions embedded within a recurrent neural network-like structure. Simulation and experiment elucidate how collective memristive switching gives rise to long-range transport pathways, drastically altering the network's global state via a discontinuous phase transition. The spatio-temporal properties of switching dynamics are found to be consistent with avalanches displaying power-law size and life-time distributions, with exponents obeying the crackling noise relationship, thus satisfying criteria for criticality. Furthermore, NWNs adaptively respond to time varying stimuli, exhibiting diverse dynamics tunable from order to chaos. Dynamical states at the edge-of-chaos are found to optimise information processing for increasingly complex learning tasks. Overall, these results reveal a rich repertoire of emergent, collective dynamics in NWNs which may be harnessed in novel, brain-inspired computing approaches
On the application of a diffusive memristor compact model to neuromorphic circuits
Memristive devices have found application in both random access memory and neuromorphic circuits. In particular, it is known that their behavior resembles that of neuronal synapses. However, it is not simple to come by samples of memristors and adjusting their parameters to change their response requires a laborious fabrication process. Moreover, sample to sample variability makes experimentation with memristor-based synapses even harder. The usual alternatives are to either simulate or emulate the memristive systems under study. Both methodologies require the use of accurate modeling equations. In this paper, we present a diffusive compact model of memristive behavior that has already been experimentally validated. Furthermore, we implement an emulation architecture that enables us to freely explore the synapse-like characteristics of memristors. The main advantage of emulation over simulation is that the former allows us to work with real-world circuits. Our results can give some insight into the desirable characteristics of the memristors for neuromorphic applications
Memristor Platforms for Pattern Recognition Memristor Theory, Systems and Applications
In the last decade a large scientific community has focused on the study of the
memristor. The memristor is thought to be by many the best alternative to CMOS
technology, which is gradually showing its flaws. Transistor technology has developed
fast both under a research and an industrial point of view, reducing the
size of its elements to the nano-scale. It has been possible to generate more and
more complex machinery and to communicate with that same machinery thanks
to the development of programming languages based on combinations of boolean
operands. Alas as shown by Mooreâs law, the steep curve of implementation and
of development of CMOS is gradually reaching a plateau. It is clear the need of
studying new elements that can combine the efficiency of transistors and at the same
time increase the complexity of the operations.
Memristors can be described as non-linear resistors capable of maintaining
memory of the resistance state that they reached. From their first theoretical treatment
by Professor Leon O. Chua in 1971, different research groups have devoted their
expertise in studying the both the fabrication and the implementation of this new
promising technology. In the following thesis a complete study on memristors
and memristive elements is presented. The road map that characterizes this study
departs from a deep understanding of the physics that govern memristors, focusing
on the HP model by Dr. Stanley Williams. Other devices such as phase change
memories (PCMs) and memristive biosensors made with Si nano-wires have been
studied, developing emulators and equivalent circuitry, in order to describe their
complex dynamics. This part sets the first milestone of a pathway that passes trough
more complex implementations such as neuromorphic systems and neural networks
based on memristors proving their computing efficiency. Finally it will be presented
a memristror-based technology, covered by patent, demonstrating its efficacy for
clinical applications. The presented system has been designed for detecting and
assessing automatically chronic wounds, a syndrome that affects roughly 2% of
the world population, through a Cellular Automaton which analyzes and processes
digital images of ulcers. Thanks to its precision in measuring the lesions the proposed
solution promises not only to increase healing rates, but also to prevent the worsening
of the wounds that usually lead to amputation and death