ハードウェアリソースの高稼働率化に基づく細粒度多値リコンフィギャラブルVLSIアーキテクチャ

Tohoku University亀山充隆課

A learning multiple-valued logic networkusing genetic algorithm

This paper describes a genetic algorithm based learning Multiple-Value Logic (MVL) network. The proposed learning network operates on a population of candidate window parameters to produce new window parameters with lower errors between the desired outputs and the actual outputs of the MVL network. Thus, the learning MVL network has a large number of search points, making it possible to obtain a global min- imum. The learning capability of the proposed MVL network with genetic algorithm is con rmed by simulations on several typical MVL functions. The simulation results show that the genetic algorithm based learning MVL network efficiently nds the appropriate network, window parameters, and bias, so that the MVL functions, especially for those relatively small problems

Mixed radix design flow for security applications

The purpose of secure devices, such as smartcards, is to protect sensitive information against software and hardware attacks. Implementation of the appropriate protection techniques often implies non-standard methods that are not supported by the conventional design tools. In the recent decade the designers of secure devices have been working hard on customising the workflow. The presented research aims at collecting the up-to-date experiences in this area and create a generic approach to the secure design flow that can be used as guidance by engineers. Well-known countermeasures to hardware attacks imply the use of specific signal encodings. Therefore, multi-valued logic has been considered as a primary aspect of the secure design. The choice of radix is crucial for multi-valued logic synthesis. Practical examples reveal that it is not always possible to find the optimal radix when taking into account actual physical parameters of multi-valued operations. In other words, each radix has its advantages and disadvantages. Our proposal is to synthesise logic in different radices, so it could benefit from their combination. With respect to the design opportunities of the existing tools and the possibilities of developing new tools that would fill the gaps in the flow, two distinct design approaches have been formed: conversion driven design and pre-synthesis. The conversion driven design approach takes the outputs of mature and time-proven electronic design automation (EDA) synthesis tools to generate mixed radix datapath circuits in an endeavour to investigate the added relative advantages or disadvantages. An algorithm underpinning the approach is presented and formally described together with secure gate-level implementations. The obtained results are reported showing an increase in power consumption, thus giving further motivation for the second approach. The pre-synthesis approach is aimed at improving the efficiency by using multivalued logic synthesis techniques to produce an abstract component-level circuit before mapping it into technology libary. Reed-Muller expansions over Galois field arithmetic have been chosen as a theoretical foundation for this approach. In order to enable the combination of radices at the mathematical level, the multi-valued Reed-Muller expansions have been developed into mixed radix Reed-Muller expansions. The goals of the work is to estimate the potential of the new approach and to analyse its impact on circuit parameters down to the level of physical gates. The benchmark results show the approach extends the search space for optimisation and provides information on how the implemented functions are related to different radices. The theory of two-level radix models and corresponding computation methods are the primary theoretical contribution. It has been implemented in RMMixed tool and interfaced to the standard EDA tools to form a complete security-aware design flow.EThOS - Electronic Theses Online ServiceEPSRCGBUnited Kingdo

A stochastic dynamic local search method for learning Multiple-Valued Logic networks

金沢大学理工研究域電子情報学系In this paper, we propose a stochastic dynamic local search (SDLS) method for Multiple-Valued Logic (MVL) learning by introducing stochastic dynamics into the traditional local search method. The proposed learning network maintains some trends of quick descent to either global minimum or a local minimum, and at the same time has some chance of escaping from local minima by permitting temporary error increases during learning. Thus the network may eventually reach the global minimum state or its best approximation with very high probability. Simulation results show that the proposed algorithm has the superior abilities to find the global minimum for the MVL network learning within reasonable number of iterations. Copyright © 2007 The Institute of Electronics, Information and Communication Engineers

The Capabilities of Chaos and Complexity

To what degree could chaos and complexity have organized a Peptide or RNA World of crude yet necessarily integrated protometabolism? How far could such protolife evolve in the absence of a heritable linear digital symbol system that could mutate, instruct, regulate, optimize and maintain metabolic homeostasis? To address these questions, chaos, complexity, self-ordered states, and organization must all be carefully defined and distinguished. In addition their cause-and-effect relationships and mechanisms of action must be delineated. Are there any formal (non physical, abstract, conceptual, algorithmic) components to chaos, complexity, self-ordering and organization, or are they entirely physicodynamic (physical, mass/energy interaction alone)? Chaos and complexity can produce some fascinating self-ordered phenomena. But can spontaneous chaos and complexity steer events and processes toward pragmatic benefit, select function over non function, optimize algorithms, integrate circuits, produce computational halting, organize processes into formal systems, control and regulate existing systems toward greater efficiency? The question is pursued of whether there might be some yet-to-be discovered new law of biology that will elucidate the derivation of prescriptive information and control. “System” will be rigorously defined. Can a low-informational rapid succession of Prigogine’s dissipative structures self-order into bona fide organization