Cellular Automata Simulation on FPGA for Training Neural Networks with Virtual World Imagery

We present ongoing work on a tool that consists of two parts: (i) A raw micro-level abstract world simulator with an interface to (ii) a 3D game engine, translator of raw abstract simulator data to photorealistic graphics. Part (i) implements a dedicated cellular automata (CA) on reconfigurable hardware (FPGA) and part (ii) interfaces with a deep learning framework for training neural networks. The bottleneck of such an architecture usually lies in the fact that transferring the state of the whole CA significantly slows down the simulation. We bypass this by sending only a small subset of the general state, which we call a 'locus of visibility', akin to a torchlight in a darkened 3D space, into the simulation. The torchlight concept exists in many games but these games generally only simulate what is in or near the locus. Our chosen architecture will enable us to simulate on a micro level outside the locus. This will give us the advantage of being able to create a larger and more fine-grained simulation which can be used to train neural networks for use in games.Comment: Published as a short paper at IEEE CIG201

Towards a Holistic CAD Platform for Nanotechnologies

Silicon-based CMOS technologies are predicted to reach their ultimate limits by the middle of the next decade. Research on nanotechnologies is actively conducted, in a world-wide effort to develop new technologies able to maintain the Moore's law. They promise revolutionizing the computing systems by integrating tremendous numbers of devices at low cost. These trends will have a profound impact on the architectures of computing systems and will require a new paradigm of CAD. The paper presents a work in progress on this direction. It is aimed at fitting requirements and constraints of nanotechnologies, in an effort to achieve efficient use of the huge computing power promised by them. To achieve this goal we are developing CAD tools able to exploit efficiently these huge computing capabilities promised by nanotechnologies in the domain of simulation of complex systems composed by huge numbers of relatively simple elements.Comment: Submitted on behalf of TIMA Editions (http://irevues.inist.fr/tima-editions

Experimental study of artificial neural networks using a digital memristor simulator

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents a fully digital implementation of a memristor hardware simulator, as the core of an emulator, based on a behavioral model of voltage-controlled threshold-type bipolar memristors. Compared to other analog solutions, the proposed digital design is compact, easily reconfigurable, demonstrates very good matching with the mathematical model on which it is based, and complies with all the required features for memristor emulators. We validated its functionality using Altera Quartus II and ModelSim tools targeting low-cost yet powerful field programmable gate array (FPGA) families. We tested its suitability for complex memristive circuits as well as its synapse functioning in artificial neural networks (ANNs), implementing examples of associative memory and unsupervised learning of spatio-temporal correlations in parallel input streams using a simplified STDP. We provide the full circuit schematics of all our digital circuit designs and comment on the required hardware resources and their scaling trends, thus presenting a design framework for applications based on our hardware simulator.Peer ReviewedPostprint (author's final draft

Analysis, design and implementation of front-end reconfigurable antenna systems (FERAS)

The increase in demand on reconfigurable systems and especially for wireless communications applications has stressed the need for smart and agile RF devices that sense and respond to the RF changes in the environment. Many different applications require frequency agility with software control ability such as in a cognitive radio environment where antenna systems have to be designed to fulfill the extendable and reconfigurable multi-service and multi-band requirements. Such applications increase spectrum efficiency as well as the power utilization in modern wireless systems. The emphasis of this dissertation revolves around the following question: Is it possible to come up with new techniques to achieve reconfigurable antenna systems with better performance?\u27 Two main branches constitute the outline of this work. The first one is based on the design of reconfigurable antennas by incorporating photoconductive switching elements in order to change the antenna electrical properties. The second branch relies on the change in the physical structure of the antenna via a rotational motion. In this work a new photoconductive switch is designed with a new light delivery technique. This switch is incorporated into new optically pumped reconfigurable antenna systems (OPRAS). The implementation of these antenna systems in applications such as cognitive radio is demonstrated and discussed. A new radio frequency (RF) technique for measuring the semiconductor carrier lifetime using optically reconfigurable transmission lines is proposed. A switching time investigation for the OPRAS is also accomplished to better cater for the cognitive radio requirements. Moreover, different reconfiguration mechanisms are addressed such as physical alteration of antenna parts via a rotational motion. This technique is supported by software to achieve a complete controlled rotatable reconfigurable cognitive radio antenna system. The inter-correlation between neural networks and cellular automata is also addressed for the design of reconfigurable and multi-band antenna systems for various applications.\u2