The problem of synthesizing a minimum-cost logic network is formulated for a genetic algorithm (GA). When benchmarked against a commercial logic synthesis tool, an odd parity circuit required 24 basic cells (BCs) versus 28 BCs for the design produced by the commercial system. A magnitude comparator required 20 BCs versus 21 BCs for the commercial system's design. Poor temporal performance, however, is the main disadvantage of the GA-based approach. The design of a hardware-based cost function that would accelerate the GA by several thousand times is described.

Entire systems on a chip (SOCs) embodying a processor, memory, and system-specific peripheral hardware are now an everyday reality. The current generation of SOC designers are driven more than ever by the need to lower chip cost, while at the same time being faced with demands to get designs to market more quickly. It was to support this new community of designers that we developed Satsuki-an integrated processor synthesis and compiler generation system. By allowing the designer to tune the processor design to the bitwidth and performance required by the application, minimum cost designs are achieved. Using synthesis to implement the processor in the same technology as the rest of the chip, allows for global chip optimization from the perspective of the system as a whole and assures design portability. The integral compiler generator, driven by the same parameters used for processor synthesis, promotes high-level expression of application algorithms while at the same time isolating the application software from the processor implementation. Synthesis experiments incorporating a 0.8 micron CMOS gate array have produced designs ranging from a 45 MHz, 1,500 gate, 8-bit processor with a 4-word register file to a 31 MHz, 9,800 gate, 32-bit processor with a 16-word register file.

Genetic algorithms were introduced by Holland in 1975 as a method of solving difficult optimization problems by means of simulated evolution. A major drawback of genetic algorithms is their slowness when emulated by software on conventional computers. Described is an adaptation of the original genetic algorithm that is advantageous to hardware implementation along with the architecture of a hardware framework that performs the functions of population storage, selection, crossover, mutation, fitness evaluation, and survival determination. Programming of the framework is illustrated with the set coverage problem that exhibits a 6,000 speed-up over software emulation on a 100 MHz workstation.

Entire systems embedded in a chip and consisting of a processor, memory, and system-specific peripheral hardware are now commonly contained in commodity electronic devices. Cost minimization of these systems is of paramount economic importance to manufactures of these devices. By employing a variable configuration processor in conjunction with a multi-precision compiler generator, we show that there are situations in which considerable system cost reduction can be obtained by synthesizing a CPU that is narrower than the largest variable in the application program.