Non-linear adaptive control inspired by neuromuscular systems

Current paradigms for neuromorphic computing focus on internal computing mechanisms, for instance using spiking-neuron models. In this study, we propose to exploit what is known about neuro-mechanical control, exploiting the mechanisms of neural ensembles and recruitment, combined with the use of second-order overdamped impulse responses corresponding to the mechanical twitches of muscle-fiber groups. Such systems may be used for controlling any analog process, by realizing three aspects: Timing, output quantity representation and wave-shape approximation. We present an electronic based model implementing a single motor unit for twitch generation. Such units can be used to construct random ensembles, separately for an agonist and antagonist 'muscle'. Adaptivity is realized by assuming a multi-state memristive system for determining time constants in the circuit. Using (Spice)-based simulations, several control tasks were implemented which involved timing, amplitude and wave shape: The inverted pendulum task, the 'whack-a-mole' task and a handwriting simulation. The proposed model can be used for both electric-to-electronic as well as electric-to-mechanical tasks. In particular, the ensemble-based approach and local adaptivity may be of use in future multi-fiber polymer or multi-actuator pneumatic artificial muscles, allowing for robust control under varying conditions and fatigue, as is the case in biological muscles

Large room-temperature tunneling anisotropic magnetoresistance and electroresistance in single ferromagnet/Nb:SrTiO3 Schottky devices

Abstract There is a large effort in research and development to realize electronic devices capable of storing information in new ways - for instance devices which simultaneously exhibit electro and magnetoresistance. However it remains a challenge to create devices in which both effects coexist. In this work we show that the well-known electroresistance in noble metal-Nb:SrTiO3 Schottky junctions can be augmented by a magnetoresistance effect in the same junction. This is realized by replacing the noble metal electrode with ferromagnetic Co. This magnetoresistance manifests as a room temperature tunneling anisotropic magnetoresistance (TAMR). The maximum room temperature TAMR (1.6%) is significantly larger and robuster with bias than observed earlier, not using Nb:SrTiO3. In a different set of devices, a thin amorphous AlOx interlayer inserted between Co and Nb:SrTiO3, reduces the TAMR by more than 2 orders of magnitude. This points to the importance of intimate contact between the Co and Nb:SrTiO3 for the TAMR effect. This is explained by electric field enhanced spin-orbit coupling of the interfacial Co layer in contact with Nb:SrTiO3. We propose that the large TAMR likely has its origin in the 3d orbital derived conduction band and large relative permittivity of Nb:SrTiO3 and discuss ways to further enhance the TAMR

Effect of swift heavy ion irradiation on surface resistance of DyBa2Cu3O7−δ thin films at microwave frequencies

We report the observation of a pronounced peak in surface resistance at microwave frequencies of 4.88 GHz and 9.55 GHz and its disappearance after irradiation with swift ions in laser ablated DyBa2Cu3O7−δ (DBCO) thin films. The measurements were carried out in zero field as well as in the presence of magnetic fields (up to 0.8 T). The films were irradiated using 90 MeV oxygen ions at Nuclear Science Centre, New Delhi at a fluence of 3 × 10^13 ions/cm2. Introduction of point defects and extended defects after irradiation suppresses the peak at 9.55 GHz whereas no suppression is observed at 4.88 GHz. These results and the vortex dynamics in the films at microwave frequencies before and after irradiation are discussed

Towards Energy Efficient Memristor-based TCAM for Match-Action Processing

Match-action processors play a crucial role of communicating end-users in the Internet by computing network paths and enforcing administrator policies. The computation process uses a specialized memory called Ternary Content Addressable Memory (TCAM) to store processing rules and use header information of network packets to perform a match within a single clock cycle. Currently, TCAM memories consume huge amounts of energy resources due to the use of traditional transistor-based CMOS technology. In this article, we motivate the use of a novel component, the memristor, for the development of a TCAM architecture. Memristors can provide energy efficiency, non-volatility, and better resource density as compared to transistors. We have proposed a novel memristor-based TCAM architecture built upon the voltage divider principle for energy efficient match-action processing. Moreover, we have tested the performance of the memristor-based TCAM architecture using the experimental data of a novel Nb-doped SrTiO3 memristor. Energy analysis of the proposed TCAM architecture for given memristor shows promising power consumption statistics of 16 μW for a match operation and 1 μW for a mismatch operation

TCA<i>m</i>M^CogniGron::Energy Efficient Memristor-Based TCAM for Match-Action Processing

The Internet relies heavily on programmable match-action processors for matching network packets against locally available network rules and taking actions, such as forwarding and modification of network packets. This match-action process must be performed at high speed, i.e., commonly within one clock cycle, using a specialized memory unit called Ternary Content Addressable Memory (TCAM). Building on transistor-based CMOS designs, state-of-the-art TCAM architectures have high energy consumption and lack resilient designs for incorporating novel technologies for performing appropriate actions. In this article, we motivate the use of a novel fundamental component, the ‘Memristor’, for the development of TCAM architecture for match-action processing. Memristors can provide energy efficiency, non-volatility and better resource density as compared to transistors. We have proposed a novel memristor-based TCAM architecture called TCAmMCogniGron, built upon the voltage divider principle and requiring only two memristors and five transistors for storage and search operations compared to sixteen transistors in the traditional TCAM architecture. We analyzed its performance over an experimental data set of Nb-doped SrTiO3-based memristor. The analysis of TCAmMCogniGron showed promising power consumption statistics of 16 uW and 1 uW for match and mismatch operations along with twice the improvement in resources density as compared to the traditional architectures

Learning to Approximate Functions Using Nb-doped SrTiO3 Memristors

Memristors have attracted interest as neuromorphic computation elements because they show promise in enabling efficient hardware implementations of artificial neurons and synapses. We performed measurements on interface-type memristors to validate their use in neuromorphic hardware. Specifically, we utilized Nb-doped SrTiO3 memristors as synapses in a simulated neural network by arranging them into differential synaptic pairs, with the weight of the connection given by the difference in normalized conductance values between the two paired memristors. This network learned to represent functions through a training process based on a novel supervised learning algorithm, during which discrete voltage pulses were applied to one of the two memristors in each pair. To simulate the fact that both the initial state of the physical memristive devices and the impact of each voltage pulse are unknown we injected noise into the simulation. Nevertheless, discrete updates based on local knowledge were shown to result in robust learning performance. Using this class of memristive devices as the synaptic weight element in a spiking neural network yields, to our knowledge, one of the first models of this kind, capable of learning to be a universal function approximator, and strongly suggests the suitability of these memristors for usage in future computing platforms