Stochastic Spin-Orbit Torque Devices as Elements for Bayesian Inference

Probabilistic inference from real-time input data is becoming increasingly popular and may be one of the potential pathways at enabling cognitive intelligence. As a matter of fact, preliminary research has revealed that stochastic functionalities also underlie the spiking behavior of neurons in cortical microcircuits of the human brain. In tune with such observations, neuromorphic and other unconventional computing platforms have recently started adopting the usage of computational units that generate outputs probabilistically, depending on the magnitude of the input stimulus. In this work, we experimentally demonstrate a spintronic device that offers a direct mapping to the functionality of such a controllable stochastic switching element. We show that the probabilistic switching of Ta/CoFeB/MgO heterostructures in presence of spin-orbit torque and thermal noise can be harnessed to enable probabilistic inference in a plethora of unconventional computing scenarios. This work can potentially pave the way for hardware that directly mimics the computational units of Bayesian inference

Analog hardware for learning neural networks

This is a recurrent or feedforward analog neural network processor having a multi-level neuron array and a synaptic matrix for storing weighted analog values of synaptic connection strengths which is characterized by temporarily changing one connection strength at a time to determine its effect on system output relative to the desired target. That connection strength is then adjusted based on the effect, whereby the processor is taught the correct response to training examples connection by connection

Stochastic Memory Devices for Security and Computing

With the widespread use of mobile computing and internet of things, secured communication and chip authentication have become extremely important. Hardware-based security concepts generally provide the best performance in terms of a good standard of security, low power consumption, and large-area density. In these concepts, the stochastic properties of nanoscale devices, such as the physical and geometrical variations of the process, are harnessed for true random number generators (TRNGs) and physical unclonable functions (PUFs). Emerging memory devices, such as resistive-switching memory (RRAM), phase-change memory (PCM), and spin-transfer torque magnetic memory (STT-MRAM), rely on a unique combination of physical mechanisms for transport and switching, thus appear to be an ideal source of entropy for TRNGs and PUFs. An overview of stochastic phenomena in memory devices and their use for developing security and computing primitives is provided. First, a broad classification of methods to generate true random numbers via the stochastic properties of nanoscale devices is presented. Then, practical implementations of stochastic TRNGs, such as hardware security and stochastic computing, are shown. Finally, future challenges to stochastic memory development are discussed

Réseaux de neurones artificiels appliqués à la méthode électromagnétique transitoire InfiniTEM

Le système InfiniTEM est une technique d’acquisition de données électromagnétiques dans le domaine temporel. Ce système est caractérisé par la forme de sa boucle émettrice qui présente des précieux avantages tels que le bon couplage avec les conducteurs sub-verticaux. L’interprétation des levés électromagnétiques en général et l’InfiniTEM en particulier constitue une tâche difficile à cause de la non-linéarité du problème à résoudre, c’est pourquoi plusieurs outils mathématiques et statistiques sont utilisés afin de faciliter la résolution des problèmes posés. Comme le but de l’interprétation est de déterminer les paramètres des conducteurs géologiques enfouis dans le sous-sol, nous avons utilisé la méthode statistique des réseaux de neurones artificiels afin d’essayer de prédire ces paramètres à partir des données InfiniTEM. Le perceptron multicouche (PMC) est l’architecture choisie pour la résolution de ce problème. La base de données créée dans la première partie de ce mémoire va jouer un rôle très important dans l’entrainement du réseau de neurones afin de prédire les propriétés des conducteurs. Une étude des paramètres du réseau a été effectuée pour voir comment ces derniers influencent les résultats de prédiction. Le nombre de couches cachées, nombre de neurones cachés, nombre d’itérations nécessaires sont des propriétés très importantes dans notre architecture, donc nous avons effectué plusieurs simulations en faisant varier chacune des propriétés précédentes. L’application de la méthode RNA à l'interprétation des données d’InfiniTEM a permis d’obtenir des résultats de prédiction très satisfaisants pour trois paramètres. L'erreur relative de la prédiction sur la conductance est inférieure à 7% tandis que l’erreur relative de prédiction sur l’inclinaison est inférieure à 10% et finalement l’erreur relative sur la profondeur est inférieure à 4%

Palmo : a novel pulsed based signal processing technique for programmable mixed-signal VLSI

In this thesis a new signal processing technique is presented. This technique exploits the use of pulses as the signalling mechanism. This Palmo 1 signalling method applied to signal processing is novel, combining the advantages of both digital and analogue techniques. Pulsed signals are robust, inherently low-power, easily regenerated, and easily distributed across and between chips. The Palmo cells used to perform analogue operations on the pulsed signals are compact, fast, simple and programmable

The hardware implementation of an artificial neural network using stochastic pulse rate encoding principles

In this thesis the development of a hardware artificial neuron device and artificial neural network using stochastic pulse rate encoding principles is considered. After a review of neural network architectures and algorithmic approaches suitable for hardware implementation, a critical review of hardware techniques which have been considered in analogue and digital systems is presented. New results are presented demonstrating the potential of two learning schemes which adapt by the use of a single reinforcement signal. The techniques for computation using stochastic pulse rate encoding are presented and extended with new novel circuits relevant to the hardware implementation of an artificial neural network. The generation of random numbers is the key to the encoding of data into the stochastic pulse rate domain. The formation of random numbers and multiple random bit sequences from a single PRBS generator have been investigated. Two techniques, Simulated Annealing and Genetic Algorithms, have been applied successfully to the problem of optimising the configuration of a PRBS random number generator for the formation of multiple random bit sequences and hence random numbers. A complete hardware design for an artificial neuron using stochastic pulse rate encoded signals has been described, designed, simulated, fabricated and tested before configuration of the device into a network to perform simple test problems. The implementation has shown that the processing elements of the artificial neuron are small and simple, but that there can be a significant overhead for the encoding of information into the stochastic pulse rate domain. The stochastic artificial neuron has the capability of on-line weight adaption. The implementation of reinforcement schemes using the stochastic neuron as a basic element are discussed