Adrian Thompson

An Evolved Circuit, Intrinsic in Silicon, Entwined with Physics

‘Intrinsic’ Hardware Evolution is the use of artificial evolution — such as a Genetic Algorithm — to design an electronic circuit automatically, where each fitness evaluation is the measurement of a circuits performance when physically instantiated in a real reconfigurable VLSI chip. This paper makes a detailed case-study of the first such application of evolution directly to the configuration of a Field Programmable Gate Array (FPGA). Evolution is allowed to explore beyond the scope of conventional design methods, resulting in a highly efficient circuit with a richer structure and dynamics and a greater respect for the natural properties of the implementation medium than is usual. The application is a simple, but not toy, problem: a tone-discrimination task. Practical details are considered throughout.

Evolutionary Robotics: the Sussex Approach

We give an overview of evolutionary robotics research at Sussex over the last five years. We explain and justify our distinctive approaches to (artificial) evolution, and to the nature of robot control systems that are evolved. Results are presented from research with evolved controllers for autonomous mobile robots, simulated robots, co-evolved animats, real robots with software controllers, and a real robot with a controller directly evolved in hardware.

Through the Labyrinth Evolution Finds a Way: A Silicon Ridge

Artificial evolution is discussed in the context of a successful experiment evolving a hardware configuration for a silicon chip (a Field Programmable Gate Array); the real chip was used to evaluate individual configurations on a tone-recognition task. The evolutionary pathway is analysed; it is shown that the population is genetically highly converged and travels far through genotype space. Species Adaptation Genetic Algorithms (SAGA) are appropriate for this type of evolution, and it is shown how an appropriate mutation rate was chosen. The role of junk on the genotype is discussed, and it is suggested that neutral networks (paths through genotype space via mutations which leave fitness unchanged) may be crucial to the effectiveness of evolution.

Evolving Electronic Robot Controller that Exploit Hardware Resources

Artificial evolution can operate upon reconfigurable electronic circuits to produce efficient and powerful control systems for autonomous mobile robots. Evolving physical hardware instead of control systems simulated in software results in more than just a raw speed increase: it is possible to exploit the physical properties of the implementation (such as the semiconductor physics of integrated circuits) to obtain control circuits of unprecedented power. The space of these evolvable circuits is far larger than the space of solutions in which a human designer works, because to make design tractable, a more abstract view than that of detailed physics must be adopted. To allow circuits to be designed at this abstract level, constraints are applied to the design that limit how the natural dynamical behaviour of the components is reflected in the overall behaviour of the system. This paper reasons that these constraints can be removed when using artificial evolution, releasing huge potential even from small circuits. Experimental evidence is given for this argument, including the first reported evolution of a real hardware control system for a real robot.

Unconstrained Evolution and Hard Consequences

Artificial evolution as a design methodology for hardware frees many of the simplifying constraints normally imposed to make design by humans tractable. However, this freedom comes at some cost, and a whole fresh set of issues must be considered. Standard genetic algorithms are not generally appropriate for hardware evolution when the number of components need not be predetermined. The use of simulations is problematic, and robustness in the presence of noise or hardware faults is important. We present theoretical arguments, and illustrate with a physical piece of hardware evolved in the real-world (‘intrinsically evolved’ hardware). A simple asynchronous digital circuit controls a real robot, using a minimal sensorimotor control system of 32 bits of RAM and a few flip-flops to co-ordinate sonar pulses and motor pulses with no further processing. This circuit is tolerant to single-stuck-at faults in the RAM. The methodology is applicable to many types of hardware, including Field-Programmable Gate Arrays (FPGAs).

On the Automatic Design of Robust Electronics Through Artificial Evolution

‘Unconstrained intrinsic hardware evolution’ allows an evolutionary algorithm freedom to find the forms and processes natural to a reconfigurable VLSI medium. It has been shown to produce highly unconventional but extremely compact FPGA configurations for simple tasks, but these circuits are usually not robust enough to be useful: they malfunction if used on a slightly different FPGA, or at a different temperature. After defining an ‘operational envelope’ of robustness, the feasibility of performing fitness evaluations in widely varying physical conditions in order to provide a selection-pressure for robustness is demonstrated. Preliminary experimental results are encouraging.

Evolution of Robustness in an Electronics Design

Evolutionary algorithms can design electronic circuits that conventional design methods cannot, because they can craft an emergent behaviour without the need for a detailed model of how the behaviours of the components affect the overall behaviour. However, the absence of such a model makes the achievement of robustness to variations in temperature, fabrication, etc., challenging. An experiment is presented showing that a robust design can be evolved without having to resort to conventional restrictive design constraints, by testing in different conditions during evolution. Surprisingly, the result tentatively suggests that even within the domain of robust digital design, evolution can explore beyond the scope of conventional methods.

Scrubbing away transients and jiggling around the permanent: long survival of FPGA systems through evolutionary self-repair

The jiggling architecture extending TMR+scrubbing is shown to mitigate FPGA transient and permanent faults using low overhead. Mission operation is never interrupted. The repair circuitry is sufficiently small that a pair could mutually repair each other. A minimal evolutionary algorithm is used during permanent fault self-repair. Reliability analysis of the studied case shows the system has a 0.99 probability of surviving 17 times the mean time to local permanent fault arrival. Such a system would be 0.99 probable to survive 100 years with one fault every 6 years.

Evolution of self-diagnosing hardware

The evolution of digital circuits performing built-in self-test behaviour is attempted in simulation for a one bit adder and a two bit multiplier. Promising results show evolved designs can perform a better diagnosis using less resources than hand-designed equivalents. Future extensions of the approach could allow the self-diagnosis of analog circuits under failure and abnormal operating conditions.

Design of single-electron systems through artificial evolution

We show how evolutionary methods can help in the design of singleelectronic circuits with an example of evolving a simple NOR gate. Evolutionary algorithms, capturing the bare essentials of Darwinian evolution, work difierently from conventional design methods, and have the potential to explore new territory. Our preliminary evolved circuit is far from an ideal NOR gate, but has interesting properties. It was evolved to work at a temperature of 340mK, and its performance deteriorates if the temperature is lowered, as well as if it is increased. This is contrary to the usual behaviour of single-electronic circuits, which generally improve with decreasing temperature. We hypothesise that the circuit exploits or relies upon the simulated efiects of the particular thermal energies of the electrons at around 340mK.