Nano-oscillator-based classification with a machine learning-compatible architecture

Pattern classification architectures leveraging the physics of coupled nano-oscillators have been demonstrated as promising alternative computing approaches, but lack effective learning algorithms. In this work, we propose a nano-oscillator based classification architecture where the natural frequencies of the oscillators are learned linear combinations of the inputs, and define an offline learning algorithm based on gradient back-propagation. Our results show significant classification improvements over a related approach with online learning. We also compare our architecture with a standard neural network on a simple machine learning case, which suggests that our approach is economical in terms of numbers of adjustable parameters. The introduced architecture is also compatible with existing nano-technologies: the architecture does not require changes in the coupling between nano-oscillators, and it is tolerant to oscillator phase noise

Circuit-Level Evaluation of the Generation of Truly Random Bits with Superparamagnetic Tunnel Junctions

Many emerging alternative models of computation require massive numbers of random bits, but their generation at low energy is currently a challenge. The superparamagnetic tunnel junction, a spintronic device based on the same technology as spin torque magnetoresistive random access memory has recently been proposed as a solution, as this device naturally switches between two easy to measure resistance states, due only to thermal noise. Reading the state of the junction naturally provides random bits, without the need of write operations. In this work, we evaluate a circuit solution for reading the state of superparamagnetic tunnel junction. We see that the circuit may induce a small read disturb effect for scaled superparamagnetic tunnel junctions, but this effect is naturally corrected in the whitening process needed to ensure the quality of the generated random bits. These results suggest that superparamagnetic tunnel junctions could generate truly random bits at 20 fJ/bit, including overheads, orders of magnitudes below CMOS-based solutions

Designing large arrays of interacting spin-torque nano-oscillators for microwave information processing

Arrays of spin-torque nano-oscillators are promising for broadband microwave signal detection and processing, as well as for neuromorphic computing. In many of these applications, the oscillators should be engineered to have equally-spaced frequencies and equal sensitivity to microwave inputs. Here we design spin-torque nano-oscillator arrays with these rules and estimate their optimum size for a given sensitivity, as well as the frequency range that they cover. For this purpose, we explore analytically and numerically conditions to obtain vortex spin-torque nano-oscillators with equally-spaced gyrotropic oscillation frequencies and having all similar synchronization bandwidths to input microwave signals. We show that arrays of hundreds of oscillators covering ranges of several hundred MHz can be built taking into account nanofabrication constraints

Physical Realization of a Supervised Learning System Built with Organic Memristive Synapses

International audienceMultiple modern applications of electronics call for inexpensive chips that can perform complex operations on natural data with limited energy. A vision for accomplishing this is implementing hardware neural networks, which fuse computation and memory, with low cost organic electronics. A challenge, however, is the implementation of synapses (analog memories) composed of such materials. In this work, we introduce robust, fastly programmable, nonvolatile organic memristive nanodevices based on electrografted redox complexes that implement synapses thanks to a wide range of accessible intermediate conductivity states. We demonstrate experimentally an elementary neural network, capable of learning functions, which combines four pairs of organic memristors as synapses and conventional electronics as neurons. Our architecture is highly resilient to issues caused by imperfect devices. It tolerates inter-device variability and an adaptable learning rule offers immunity against asymmetries in device switching. Highly compliant with conventional fabrication processes, the system can be extended to larger computing systems capable of complex cognitive tasks, as demonstrated in complementary simulations

Rythmes et oscillations : une vision pour la nanoélectronique

With the advent of "artificial intelligence", computers, mobile devices and other connected objects are being pushed beyond the realm of arithmetic and logic operations, for which they have been optimized over decades, in order to process "cognitive" tasks such as automatic translation and image or voice recognition, for which they are not the ideal substrate. As a result, supercomputers may require megawatts to process tasks for which the human brain only needs 20 watt. This has revived interest into the design of alternative computing schemes inspired by the brain. In particular, neural oscillations that appear to be linked to computational activity in the brain have inspired approaches leveraging the complex physics of networks of coupled oscillators in order to process cognitive tasks efficiently. In the light of recent advances in nano-technology allowing the fabrication of highly integrable nano-oscillators, this thesis proposes and studies novel neuro-inspired oscillator-based pattern classification architectures that could be implemented on chip.Avec l'avènement de l'"intelligence artificielle", les ordinateurs, appareils mobiles et objets connectés sont amenés à dépasser les calculs arithmétiques et logiques pour lesquels ils ont été optimisés durant des décennies, afin d'effectuer des tâches "cognitives" telles que la traduction automatique ou la reconnaissance d'images et de voix, et pour lesquelles ils ne sont pas adaptés. Ainsi, un super-calculateur peut-il consommer des mégawatts pour effectuer des tâches que le cerveau humain traite avec 20 watt. Par conséquent, des système de calcul alternatifs inspirés du cerveau font l'objet de recherches importantes. En particulier, les oscillations neurales semblant être liées à certains traitements de données dans le cerveau ont inspiré des approches détournant la physique complexe des réseaux d'oscillateurs couplés pour effectuer des tâches cognitives efficacement. Cette thèse se fonde sur les avancées récentes en nano-technologies permettant la fabrication de nano-oscillateurs hautement intégrables pour proposer et étudier de nouvelles architectures neuro-inspirées de classification de motifs exploitant la dynamique des oscillateurs couplés et pouvant être implémentées sur puce

A Nanotechnology-Ready Computing Scheme based on a Weakly Coupled Oscillator Network

With conventional transistor technologies reaching their limits, alternative computing schemes based on novel technologies are currently gaining considerable interest. Notably, promising computing approaches have proposed to leverage the complex dynamics emerging in networks of coupled oscillators based on nanotechnologies. The physical implementation of such architectures remains a true challenge, however, as most proposed ideas are not robust to nanotechnology devices’ non-idealities. In this work, we propose and investigate the implementation of an oscillator-based architecture, which can be used to carry out pattern recognition tasks, and which is tailored to the specificities of nanotechnologies. This scheme relies on a weak coupling between oscillators, and does not require a fine tuning of the coupling values. After evaluating its reliability under the severe constraints associated to nanotechnologies, we explore the scalability of such an architecture, suggesting its potential to realize pattern recognition tasks using limited resources. We show that it is robust to issues like noise, variability and oscillator non-linearity. Defining network optimization design rules, we show that nano-oscillator networks could be used for efficient cognitive processing

Vortex-based spin transfer oscillator compact model for IC design

International audience—Spintronic oscillators are nanodevices that are serious candidates for CMOS integration due to their compactness and easy frequency tunability. Among them vortex-based oscilla-tors appear as one of the most promising technology because of their lower power supply and higher quality factors. To assess their potential in circuits and systems, compact models describing their behavior are necessary. In this work, we propose an implementation of a spintronic nano-oscillator (STNO) model for integrated circuit (IC) architectures design. The modeled device is a vortex-based magnetic oscillator demonstrating self-sustained magnetization oscillations under current bias, inducing alternating voltage across the device. This model describes the coupled electrical and magnetic behavior of the device, taking into account phase and amplitude noises associated with thermal fluctuations. Compatibility with commercial CMOS design kits is demonstrated, and an implementation in a CMOS circuit is proposed for AC signal generation. These results will allow to develop and evaluate innovative hybrid STNO/CMOS systems and their potential to efficiently complement existing full-CMOS technologies

Spintronic Devices as Key Elements for Energy-Efficient Neuroinspired Architectures

International audience—Processing the current deluge of data using conventional CMOS architectures requires a tremendous amount of energy, as it is inefficient for tasks such as data mining, recognition and synthesis. Alternative models of computation based on neuroinspiration can prove much more efficient for these kinds of tasks, but do not map ideally to traditional CMOS. Spintronics, by contrast, can bring features such as embedded nonvolatile memory and stochastic and memristive behavior, which, when associated with CMOS, can be key enablers for neuroinspired computing. In this paper, we explore different works that go in this direction. First, we illustrate how recent developments in embedded nonvolatile memory based on magnetic tunnel junctions (MTJs) can provide the large amount of nonvolatile memory required in neuro-inspired designs while avoiding Von Neumann bottleneck. Second, we show that recently developed spintronic memristors can implement artificial synapses for neuromorphic systems. With a more groundbreaking design, we show how the probabilistic writing of single MTJ bits can efficiently replace multi-level weighting for some classes of neuroinspired architectures. Finally, we show that a special class of MTJs can exhibit the phenomenon of stochastic resonance, a strategy used in biological systems to detect weak signals. These results suggest that the impact of spintronics extends beyond the traditional standalone and embedded memory markets