Cellular Logic Machines

Abstract

Chapter 1 describes the early work of von Neumann (1951) on cellular automata. This work was not accompanied by reductions to practice in hardware as it was impractical at that time to build machines having the millions of devices required. Only with the development of high-density integrated circuitry in the 1970s has this feat now been accomplished (Chapter 11). Therefore, during the 1950s cellular automata were emulated using the general-purpose computers which were available at that time. The work of Moore (1966) and Kirsch (1957) at the National Bureau of Standards, of Ulam (1962) at the Atomic Energy Commission, and Unger (1959) at Bell Telephone Laboratories are outstanding examples of this work. All of these workers simplified von Neumann’s 29-state processing element and concentrated their efforts on studying arrays of 2-state (binary) processing elements. In the 1960s a new trend began with the construction of the first cellular logic machines by one of the authors (Preston, 1961). These and other special-purpose machines emulated the cellular automaton by using a single high-speed processing element to operate sequentially on an array of binary data. With the introduction of the diff3 in the 1970s (Graham, and Norgren, 1980) cellular logic machines were manufactured having several processing elements. Then Sternberg (1981) introduced a pipelined cellular logic machine, called the Cytocomputer, having approximately 100 processing elements. The Cytocomputer was also the first cellular logic machine to include numerical (multi-state) processing elements in addition to binary processing elements, thus making the transition from high-speed special-purpose machines limited to bilevel data to a system which could manipulate multi-state data. This chapter concentrates on the evolution and architecture of the cellular logic machines which have been built over the past two decades, all of which handle bilevel data arrays. Machines of this kind are currently in wide use both for commercial and research purposes in image processing. Despite their limitation to bilevel data, they are also useful in graylevel image processing due to their ability to convert graylevel images to binary images by multiple thresholding and, after performing logical operations at these thresholds, to convert results to graylevel output by arithmetic summation (Chapter 2).