On the scalability of evolvable hardware architectures: comparison of systolic array and Cartesian genetic programming

Abstract

Evolvable hardware allows generating circuits adapted to specific problems by using an evolutionary algorithm (EA). Dynamic partial reconfiguration of FPGA LUTs allows making the processing elements (PEs) of these circuits small and compact, thus allowing large scale circuits to be implemented in a small FPGA area. This facilitates the use of these techniques in embedded systems with limited resources.The improvement on resource-efficient implementation techniques has allowed increasing the size of processing architectures from a few PEs to several hundreds. However, these large sizes pose new challenges for the EA and the architecture, which may not be able to take full advantage of the computing capabilities of its PEs.In this article, two different topologies-systolic array (SA) and Cartesian genetic programming (CGP)-are scaled from small to large sizes and analyzed, comparing their behavior and efficiency at different sizes. Additionally, improvements on SA connectivity are studied.Experimental results show that, in general, SA is considerably more resource-efficient than CGP, needing up to 60% fewer FPGA resources (LUTs) for a solution with similar performance, since the LUT usage per PE is 5 times smaller. Specifically, 10×10 SA has better performance than 5×10 CGP, but uses 50% fewer resources.

DEHypGpOls: A Genetic Programming with Evolutionary Hyper-Parameter Optimization and its Application for Stock Market Trend Prediction

“…Also, GP has been widely utilized in many computer science problems such as classification problems (Tran et al 2016) (Kuo et al 2007), computer vision (Liu et al 2016), image processing (Shao et al 2014) (Liang et al 2020), signal processing (Feli and Abdali-Mohammadi 2019), artificial neural network design (Suganuma et al 2017). Moreover, GP methods were used in the field of evolutionary hardware (Mora et al 2019) and circuit design (Sikulova et al 2014) (Koza et al 2004). One can find many application of GP in the field of economy (e.g., financial fraud detection (Li and Wong 2015) and green supplier selection (Fallahpour et al 2016) ) FigureFigure 2 shows an example syntax tree of a GP model.…”

DEHypGpOls: a genetic programming with evolutionary hyperparameter optimization and its application for stock market trend prediction

“…Also, GP has been widely utilized in many computer science problems such as classification problems (Tran et al 2016) (Kuo et al 2007), computer vision (Liu et al 2016), image processing (Shao et al 2014) (Liang et al 2020), signal processing (Feli and Abdali-Mohammadi 2019), artificial neural network design (Suganuma et al 2017). Moreover, GP methods were used in the field of evolutionary hardware (Mora et al 2019) and circuit design (Sikulova et al 2014) (Koza et al 2004). One can find many application of GP in the field of economy (e.g., financial fraud detection (Li and Wong 2015) and green supplier selection (Fallahpour et al 2016) ) FigureFigure 2 shows an example syntax tree of a GP model.…”

A Review of Genetic Programming: Popular Techniques, Fundamental Aspects, Software Tools and Applications

“…Circuit Design and Evolvable hardware: Evolutionary Hardware (EHW) is a design approach that uses a reconfigurable hardware structure to develop a circuit that performs a specific function. Hardware can be designed automatically by using GP algorithms without the need for a circuit designer [22]. Due to convenience of program representations to express hardware, the CGP method is employed extensively in circuit design works.…”

“…Due to convenience of program representations to express hardware, the CGP method is employed extensively in circuit design works. As a consequence, the GP has been widely utilized in EHW studies [22], [81], [82] Besides, the GP is frequently used in digital circuit design tasks [23], [83]- [85].…”

“…Additionally, GP has widely utilized in many computer sciences problems such as in computer vision [18], image processing [19], signal processing [20], artificial neural network design [21]. Moreover, GP methods were used in the field of evolutionary hardware [22] and circuit design [23]. One can find many application of GP in the field of economy e.g.…”