Estimators for Logic Minimization and Implementation Selection of Finite State machines

This paper considers two estimation problems which occur during the implementation design for a finite state machine (FSM). The first is a precise estimation of the reduction of a programmed logic array implementation (PLA) for a FSM by logic minimization. The second concerns selection of implementation alternatives based on such estimations. Estimations give the designer a quick overview of the impact of an optimization method for FSM implementation without running the actual time-consuming algorithms. The method uses curve-fitting on results found in literature for logic minimization preceded by state-assignment. Our estimations correlate by 0.97 to those results. State-graph statistics can also be used for selection of the most profitable optimization from a set of alternatives. We tested selection between a counter based implementation, partial state coding, state-assignment and topological partitioning. The goal is selection of the alternative which has the highest probability to deliver the largest minimization of the FSM. This selection method is also empirically verified by comparing its results with results obtained by running specific optimization algorithms on machines of the MCNC benchmark set

Neural-network dedicated processor for solving competitive assignment problems

A neural-network processor for solving first-order competitive assignment problems consists of a matrix of N x M processing units, each of which corresponds to the pairing of a first number of elements of (R sub i) with a second number of elements (C sub j), wherein limits of the first number are programmed in row control superneurons, and limits of the second number are programmed in column superneurons as MIN and MAX values. The cost (weight) W sub ij of the pairings is programmed separately into each PU. For each row and column of PU's, a dedicated constraint superneuron insures that the number of active neurons within the associated row or column fall within a specified range. Annealing is provided by gradually increasing the PU gain for each row and column or increasing positive feedback to each PU, the latter being effective to increase hysteresis of each PU or by combining both of these techniques

Towards heterotic computing with droplets in a fully automated droplet-maker platform

The control and prediction of complex chemical systems is a difficult problem due to the nature of the interactions, transformations and processes occurring. From self-assembly to catalysis and self-organization, complex chemical systems are often heterogeneous mixtures that at the most extreme exhibit system-level functions, such as those that could be observed in a living cell. In this paper, we outline an approach to understand and explore complex chemical systems using an automated droplet maker to control the composition, size and position of the droplets in a predefined chemical environment. By investigating the spatio-temporal dynamics of the droplets, the aim is to understand how to control system-level emergence of complex chemical behaviour and even view the system-level behaviour as a programmable entity capable of information processing. Herein, we explore how our automated droplet-maker platform could be viewed as a prototype chemical heterotic computer with some initial data and example problems that may be viewed as potential chemically embodied computations

Hierarchical stack filtering : a bitplane-based algorithm for massively parallel processors

With the development of novel parallel architectures for image processing, the implementation of well-known image operators needs to be reformulated to take advantage of the so-called massive parallelism. In this work, we propose a general algorithm that implements a large class of nonlinear filters, called stack filters, with a 2D-array processor. The proposed method consists of decomposing an image into bitplanes with the bitwise decomposition, and then process every bitplane hierarchically. The filtered image is reconstructed by simply stacking the filtered bitplanes according to their order of significance. Owing to its hierarchical structure, our algorithm allows us to trade-off between image quality and processing time, and to significantly reduce the computation time of low-entropy images. Also, experimental tests show that the processing time of our method is substantially lower than that of classical methods when using large structuring elements. All these features are of interest to a variety of real-time applications based on morphological operations such as video segmentation and video enhancement

Stochastic assembly of sublithographic nanoscale interfaces

We describe a technique for addressing individual nanoscale wires with microscale control wires without using lithographic-scale processing to define nanoscale dimensions. Such a scheme is necessary to exploit sublithographic nanoscale storage and computational devices. Our technique uses modulation doping to address individual nanowires and self-assembly to organize them into nanoscale-pitch decoder arrays. We show that if coded nanowires are chosen at random from a sufficiently large population, we can ensure that a large fraction of the selected nanowires have unique addresses. For example, we show that N lines can be uniquely addressed over 99% of the time using no more than /spl lceil/2.2log/sub 2/(N)/spl rceil/+11 address wires. We further show a hybrid decoder scheme that only needs to address N=O(W/sub litho-pitch//W/sub nano-pitch/) wires at a time through this stochastic scheme; as a result, the number of unique codes required for the nanowires does not grow with decoder size. We give an O(N/sup 2/) procedure to discover the addresses which are present. We also demonstrate schemes that tolerate the misalignment of nanowires which can occur during the self-assembly process

Deterministic Addressing of Nanoscale Devices Assembled at Sublithographic Pitches

Multiple techniques have now been proposed using random addressing to build demultiplexers which interface between the large pitch of lithographically patterned features and the smaller pitch of self-assembled sublithographic nanowires. At the same time, the relatively high defect rates expected for molecular-sized devices and wires dictate that we design architectures with spare components so we can map around defective elements. To accommodate and mask both of these effects, we introduce a programmable addressing scheme which can be used to provide deterministic addresses for decoders built with random nanoscale addressing and potentially defective wires. We describe how this programmable addressing scheme can be implemented with emerging, nanoscale building blocks and show how to build deterministically addressable memory banks. We characterize the area required for this programmable addressing scheme. For 2048 x 2048 memory banks, the area overhead for address correction is less than 33%, delivering net memory densities around 10^11 b/cm^2