6,911 research outputs found
Feedback control by online learning an inverse model
A model, predictor, or error estimator is often used by a feedback controller to control a plant. Creating such a model is difficult when the plant exhibits nonlinear behavior. In this paper, a novel online learning control framework is proposed that does not require explicit knowledge about the plant. This framework uses two learning modules, one for creating an inverse model, and the other for actually controlling the plant. Except for their inputs, they are identical. The inverse model learns by the exploration performed by the not yet fully trained controller, while the actual controller is based on the currently learned model. The proposed framework allows fast online learning of an accurate controller. The controller can be applied on a broad range of tasks with different dynamic characteristics. We validate this claim by applying our control framework on several control tasks: 1) the heating tank problem (slow nonlinear dynamics); 2) flight pitch control (slow linear dynamics); and 3) the balancing problem of a double inverted pendulum (fast linear and nonlinear dynamics). The results of these experiments show that fast learning and accurate control can be achieved. Furthermore, a comparison is made with some classical control approaches, and observations concerning convergence and stability are made
An Efficient Primal-Dual Prox Method for Non-Smooth Optimization
We study the non-smooth optimization problems in machine learning, where both
the loss function and the regularizer are non-smooth functions. Previous
studies on efficient empirical loss minimization assume either a smooth loss
function or a strongly convex regularizer, making them unsuitable for
non-smooth optimization. We develop a simple yet efficient method for a family
of non-smooth optimization problems where the dual form of the loss function is
bilinear in primal and dual variables. We cast a non-smooth optimization
problem into a minimax optimization problem, and develop a primal dual prox
method that solves the minimax optimization problem at a rate of
{assuming that the proximal step can be efficiently solved}, significantly
faster than a standard subgradient descent method that has an
convergence rate. Our empirical study verifies the efficiency of the proposed
method for various non-smooth optimization problems that arise ubiquitously in
machine learning by comparing it to the state-of-the-art first order methods
A Novel FPGA Implementation of Hierarchical Temporal Memory Spatial Pooler
There is currently a strong focus across the technological landscape to create machines capable of performing complex, objective based tasks in a manner similar to, or superior to a human. Many of the methods being explored in the machine intelligence space require large sets of labeled data to first train, and then classify inputs. Hierarchical Temporal Memory (HTM) is a biologically inspired machine intelligence framework which aims to classify and interpret streaming unlabeled data, without supervision, and be able to detect anomalies in such data.
In software HTM models, increasing the number of “columns” or processing elements to the levels required to make meaningful predictions in complex data can be prohibitive to analyzing in real time. There exists a need to improve the throughput of such systems. HTMs require large amounts of data available to be accessed randomly, and then processed independently. FPGAs provide a reconfigurable, and easily scalable platform ideal for these types of operations. One of the two main components of the HTM architecture is the “spatial pooler”. This thesis explores a novel hardware implementation of an HTM spatial pooler, with a boosting algorithm to increase homeostasis, and a novel classification algorithm to interpret input data in real time. This implementation shows a significant speedup in data processing, and provides a framework to scale the implementation based on the available hardware resources of the FPGA
Integer Echo State Networks: Hyperdimensional Reservoir Computing
We propose an approximation of Echo State Networks (ESN) that can be
efficiently implemented on digital hardware based on the mathematics of
hyperdimensional computing. The reservoir of the proposed Integer Echo State
Network (intESN) is a vector containing only n-bits integers (where n<8 is
normally sufficient for a satisfactory performance). The recurrent matrix
multiplication is replaced with an efficient cyclic shift operation. The intESN
architecture is verified with typical tasks in reservoir computing: memorizing
of a sequence of inputs; classifying time-series; learning dynamic processes.
Such an architecture results in dramatic improvements in memory footprint and
computational efficiency, with minimal performance loss.Comment: 10 pages, 10 figures, 1 tabl
Cloud-efficient modelling and simulation of magnetic nano materials
Scientific simulations are rarely attempted in a cloud due to the substantial
performance costs of virtualization. Considerable communication overheads,
intolerable latencies, and inefficient hardware emulation are the main reasons why
this emerging technology has not been fully exploited. On the other hand, the
progress of computing infrastructure nowadays is strongly dependent on
perspective storage medium development, where efficient micromagnetic
simulations play a vital role in future memory design.
This thesis addresses both these topics by merging micromagnetic simulations
with the latest OpenStack cloud implementation while providing a time and costeffective alternative to expensive computing centers.
However, many challenges have to be addressed before a high-performance cloud
platform emerges as a solution for problems in micromagnetic research
communities. First, the best solver candidate has to be selected and further
improved, particularly in the parallelization and process communication domain.
Second, a 3-level cloud communication hierarchy needs to be recognized and
each segment adequately addressed. The required steps include breaking the VMisolation for the host’s shared memory activation, cloud network-stack tuning,
optimization, and efficient communication hardware integration.
The project work concludes with practical measurements and confirmation of
successfully implemented simulation into an open-source cloud environment. It is
achieved that the renewed Magpar solver runs for the first time in the OpenStack
cloud by using ivshmem for shared memory communication. Also, extensive
measurements proved the effectiveness of our solutions, yielding from sixty
percent to over ten times better results than those achieved in the standard cloud.Aufgrund der erheblichen Leistungskosten der Virtualisierung werden
wissenschaftliche Simulationen in einer Cloud selten versucht. Beträchtlicher
Kommunikationsaufwand, erhebliche Latenzen und ineffiziente
Hardwareemulation sind die HauptgrĂĽnde, warum diese aufkommende
Technologie nicht vollständig genutzt wurde. Andererseits hängt der Fortschritt der
Computertechnologie heutzutage stark von der Entwicklung perspektivischer
Speichermedien ab, bei denen effiziente mikromagnetische Simulationen eine
wichtige Rolle fĂĽr die zukĂĽnftige Speichertechnologie spielen.
Diese Arbeit befasst sich mit diesen beiden Themen, indem mikromagnetische
Simulationen mit der neuesten OpenStack Cloud-Implementierung
zusammengefĂĽhrt werden, um eine zeit- und kostengĂĽnstige Alternative zu teuren
Rechenzentren bereitzustellen.
Viele Herausforderungen mĂĽssen jedoch angegangen werden, bevor eine
leistungsstarke Cloud-Plattform als Lösung für Probleme in mikromagnetischen
Forschungsgemeinschaften entsteht. Zunächst muss der beste Kandidat für die
Lösung ausgewählt und weiter verbessert werden, insbesondere im Bereich der
Parallelisierung und Prozesskommunikation. Zweitens muss eine 3-stufige CloudKommunikationshierarchie erkannt und jedes Segment angemessen adressiert
werden. Die erforderlichen Schritte umfassen das Aufheben der VM-Isolation, um
den gemeinsam genutzten Speicher zwischen Cloud-Instanzen zu aktivieren, die
Optimierung des Cloud-Netzwerkstapels und die effiziente Integration von
Kommunikationshardware.
Die praktische Arbeit endet mit Messungen und der Bestätigung einer erfolgreich
implementierten Simulation in einer Open-Source Cloud-Umgebung. Als Ergebnis
haben wir erreicht, dass der neu erstellte Magpar-Solver zum ersten Mal in der
OpenStack Cloud ausgefĂĽhrt wird, indem ivshmem fĂĽr die Shared-Memory
Kommunikation verwendet wird. Umfangreiche Messungen haben auch die
Wirksamkeit unserer Lösungen bewiesen und von sechzig Prozent bis zu zehnmal
besseren Ergebnissen als in der Standard Cloud gefĂĽhrt
Scalable Digital Architecture of a Liquid State Machine
Liquid State Machine (LSM) is an adaptive neural computational model with rich dynamics to process spatio-temporal inputs. These machines are extremely fast in learning because the goal-oriented training is moved to the output layer, unlike conventional recurrent neural networks.
The capability to multiplex at the output layer for multiple tasks makes LSM a powerful intelligent engine. These properties are desirable in several machine learning applications such as speech recognition, anomaly detection, user identification etc. Scalable hardware architectures for spatio-temporal signal processing algorithms like LSMs are energy efficient compared to the software implementations. These designs can also naturally adapt to dierent temporal streams of inputs. Early literature shows few behavioral models of LSM. However, they cannot process real time data either due to their hardware complexity or xed design approach. In this thesis, a scalable digital architecture of an LSM is proposed. A key feature of the architecture is a digital liquid that exploits spatial locality and is capable of processing real time data. The quality of the proposed LSM is analyzed using kernel quality, separation property of the liquid and Lyapunov exponent. When realized using TSMC 65nm technology node, the total power dissipation of the liquid layer, with 60 neurons, is 55.7 mW with an area requirement of 2 mm^2. The proposed model is validated for two benchmark. In the case of an epileptic seizure detection an average accuracy of 84% is observed. For user identification/authentication using gait an average accuracy of 98.65% is achieved
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