Quantum Memristors

Technology based on memristors, resistors with memory whose resistance depends on the history of the crossing charges, has lately enhanced the classical paradigm of computation with neuromorphic architectures. However, in contrast to the known quantized models of passive circuit elements, such as inductors, capacitors or resistors, the design and realization of a quantum memristor is still missing. Here, we introduce the concept of a quantum memristor as a quantum dissipative device, whose decoherence mechanism is controlled by a continuous-measurement feedback scheme, which accounts for the memory. Indeed, we provide numerical simulations showing that memory effects actually persist in the quantum regime. Our quantization method, specifically designed for superconducting circuits, may be extended to other quantum platforms, allowing for memristor-type constructions in different quantum technologies. The proposed quantum memristor is then a building block for neuromorphic quantum computation and quantum simulations of non-Markovian systems

3D printed neuromorphic sensing systems

Thanks to the high energy efficiency, neuromorphic devices are spotlighted recently by mimicking the calculation principle of the human brain through the parallel computation and the memory function. Various bio-inspired \u27in-memory computing\u27 (IMC) devices were developed during the past decades, such as synaptic transistors for artificial synapses. By integrating with specific sensors, neuromorphic sensing systems are achievable with the bio-inspired signal perception function. A signal perception process is possible by a combination of stimuli sensing, signal conversion/transmission, and signal processing. However, most neuromorphic sensing systems were demonstrated without signal conversion/transmission functions. Therefore, those cannot fully mimic the function provides by the sensory neuron in the biological system. This thesis aims to design a neuromorphic sensing system with a complete function as biological sensory neurons. To reach such a target, 3D printed sensors, electrical oscillators, and synaptic transistors were developed as functions of artificial receptors, artificial neurons, and artificial synapses, respectively. Moreover, since the 3D printing technology has demonstrated a facile process due to fast prototyping, the proposed 3D neuromorphic sensing system was designed as a 3D integrated structure and fabricated by 3D printing technologies. A novel multi-axis robot 3D printing system was also utilized to increase the fabrication efficiency with the capability of printing on vertical and tilted surfaces seamlessly. Furthermore, the developed 3D neuromorphic system was easily adapted to the application of tactile sensing. A portable neuromorphic system was integrated with a tactile sensing system for the intelligent tactile sensing application of the humanoid robot. Finally, the bio-inspired reflex arc for the unconscious response was also demonstrated by training the neuromorphic tactile sensing system

The Computational Capacity of Memristor Reservoirs

Reservoir computing is a machine learning paradigm in which a high-dimensional dynamical system, or \emph{reservoir}, is used to approximate and perform predictions on time series data. Its simple training procedure allows for very large reservoirs that can provide powerful computational capabilities. The scale, speed and power-usage characteristics of reservoir computing could be enhanced by constructing reservoirs out of electronic circuits, but this requires a precise understanding of how such circuits process and store information. We analyze the feasibility and optimal design of such reservoirs by considering the equations of motion of circuits that include both linear elements (resistors, inductors, and capacitors) and nonlinear memory elements (called memristors). This complements previous studies, which have examined such systems through simulation and experiment. We provide analytic results regarding the fundamental feasibility of such reservoirs, and give a systematic characterization of their computational properties, examining the types of input-output relationships that may be approximated. This allows us to design reservoirs with optimal properties in terms of their ability to reconstruct a certain signal (or functions thereof). In particular, by introducing measures of the total linear and nonlinear computational capacities of the reservoir, we are able to design electronic circuits whose total computation capacity scales linearly with the system size. Comparison with conventional echo state reservoirs show that these electronic reservoirs can match or exceed their performance in a form that may be directly implemented in hardware.Comment: 18 pages double column

Everything You Wish to Know About Memristors But Are Afraid to Ask

This paper classifies all memristors into three classes called Ideal, Generic, or Extended memristors. A subclass of Generic memristors is related to Ideal memristors via a one-to-one mathematical transformation, and is hence called Ideal Generic memristors. The concept of non-volatile memories is defined and clarified with illustrations. Several fundamental new concepts, including Continuum-memory memristor, POP (acronym for Power-Off Plot), DC V-I Plot, and Quasi DC V-I Plot, are rigorously defined and clarified with colorful illustrations. Among many colorful pictures the shoelace DC V-I Plot stands out as both stunning and illustrative. Even more impressive is that this bizarre shoelace plot has an exact analytical representation via 2 explicit functions of the state variable, derived by a novel parametric approach invented by the author

Cryogenic Neuromorphic Hardware

The revolution in artificial intelligence (AI) brings up an enormous storage and data processing requirement. Large power consumption and hardware overhead have become the main challenges for building next-generation AI hardware. To mitigate this, Neuromorphic computing has drawn immense attention due to its excellent capability for data processing with very low power consumption. While relentless research has been underway for years to minimize the power consumption in neuromorphic hardware, we are still a long way off from reaching the energy efficiency of the human brain. Furthermore, design complexity and process variation hinder the large-scale implementation of current neuromorphic platforms. Recently, the concept of implementing neuromorphic computing systems in cryogenic temperature has garnered intense interest thanks to their excellent speed and power metric. Several cryogenic devices can be engineered to work as neuromorphic primitives with ultra-low demand for power. Here we comprehensively review the cryogenic neuromorphic hardware. We classify the existing cryogenic neuromorphic hardware into several hierarchical categories and sketch a comparative analysis based on key performance metrics. Our analysis concisely describes the operation of the associated circuit topology and outlines the advantages and challenges encountered by the state-of-the-art technology platforms. Finally, we provide insights to circumvent these challenges for the future progression of research

Neuromorphic, Digital and Quantum Computation with Memory Circuit Elements

Memory effects are ubiquitous in nature and the class of memory circuit elements - which includes memristors, memcapacitors and meminductors - shows great potential to understand and simulate the associated fundamental physical processes. Here, we show that such elements can also be used in electronic schemes mimicking biologically-inspired computer architectures, performing digital logic and arithmetic operations, and can expand the capabilities of certain quantum computation schemes. In particular, we will discuss few examples where the concept of memory elements is relevant to the realization of associative memory in neuronal circuits, spike-timing-dependent plasticity of synapses, digital and field-programmable quantum computing

The Department of Electrical and Computer Engineering Newsletter

Summer 2017 News and notes for University of Dayton\u27s Department of Electrical and Computer Engineering.https://ecommons.udayton.edu/ece_newsletter/1010/thumbnail.jp

Physical Model for the Current–Voltage Hysteresis and Impedance of Halide Perovskite Memristors

An investigation of the kinetic behavior of MAPbI3 memristors shows that the onset voltage to a high conducting state depends strongly on the voltage sweep rate, and the impedance spectra generate complex capacitive and inductive patterns. We develop a dynamic model to describe these features and obtain physical insight into the coupling of ionic and electronic properties that produce the resistive switching behavior. The model separates the memristive response into distinct diffusion and transition-state-formation steps that describe well the experimental current–voltage curves at different scan rates and impedance spectra. The ac impedance analysis shows that the halide perovskite memristor response contains the composition of two inductive processes that provide a huge negative capacitance associated with inverted hysteresis. The results provide a new approach to understand some typical characteristics of halide perovskite devices, such as the inductive behavior and hysteresis effects, according to the time scales of internal processes.Funding for open access charge: CRUE-Universitat Jaume IWe acknowledge the financial support from Generalitat Valenciana for a Grisolia grant (GRISOLIAP/2019/048) and Ministerio de Ciencia y Innovación (PID2019-107348GB-100). We also acknowledge the financial support of CONICET (Extern Fellowship 2020); Comunidad de Madrid (S2018/NMT-4326-SINFOTON2-CM); and Universidad Rey Juan Carlos “Grupo DELFO de alto rendimiento”, reference M2363, under research program “Programa de fomento y desarrollo de la investigación”