Chapter 13 - Evolutionary Mapping Techniques for Systolic Computing System

Abstract

Abstract Systolic arrays are hardware structures built for fast and efficient operation of regular algorithms that perform the same task with different data at different time instants. Systolic arrays replace a pipeline structure with an array of processing elements that can be programmed to perform a common operation. Regularity, reconfigurability and scalability are some of the features of systolic design. Systolic architectures offer the competence to uphold the high-throughput capacity requirement. Multi-dimensional image processing algorithms, video streaming, nonlinear optimization problems and decision based algorithms are a few of many algorithms that are computationally demanding and can be benefited by implementing systolic arrays. To satisfy a highly held comparison parameter of computational efficiency, there exists a bottleneck of memory hierarchy. It is undeniable that hardware and software have to go hand in hand to remove the bottleneck and achieve better performance. Any regular algorithm such as matrix multiplication can be implemented in systolic architecture through mapping hardware computations to a space time transformation using a dependence graph. Systolic design methodology maps an N-dimensional dependence graph to a lower-dimensional systolic architecture using a transformation. Mapping is a process of assigning each point in iteration space a scheduled processing element for the operation at discrete time. Mapping can be done heuristically with high cost in accuracy and design time. Evolutionary algorithms act as an alternative solution for efficient search for mapping solutions. The evolutionary algorithms belong to non-traditional techniques which mimic the biological behavior of organisms to obtain the solution. They duplicate the nature of species evolution, group of ants, swarm of birds, school of fishes, groups of frogs, etc. The decision of choosing an evolutionary algorithm for the mapping process is based on its swift learning capabilities and less computation time compared to traditional random, exhaustive search procedures. Evolutionary algorithms start with a population of possible solutions and, through biological operators such as crossover, mutation, evolution based on social behavior and personal experience, the algorithm moves to a better solution. Differential evolution, bacterial foraging optimization, bees algorithm, genetic algorithm, particle swarm optimization, memetic algorithm, ant colony optimization, and shuffled frog leaping algorithm are some of the evolutionary algorithms, and a few of the listed processes are discussed in this chapter. This discussion is an accolade for an architecture that has been developed for the main reason of improving the hardware utilization efficiency, cost effectiveness and performance of iterative algorithms. Grey tone difference matrix generation in texture analysis [1] is taken as an example to prove the efficiency of systolic arrays that are mapped with evolutionary algorithms. Parameters for comparing the efficiency of the algorithms are the number of iterations for which there has been no improvement in the cost function, mapping matrices, average value of the cost function and processing time in achieving the desired result.