Biological systems hold the key to ultra-powerful computing using just microwatts of power; this research explores emulating this behavior in electrical systems. A novel, biologically inspired computing paradigm is being developed which could fundamentally change scientific and multimedia computing. This new computing approach simultaneously addresses a looming problem in semiconductor systems and many nano-electronics systems; that is, as feature sizes in computer chips are scaled down further, ideal behavior cannot always be guaranteed. In these systems, results of individual operations are described only statistically or probabilistically. A principal point of this research is to embrace probabilistic computing elements rather than to devise ways to make them ideal or deterministic. Recent research suggests that biological and other natural systems are probabilistic in nature. Thus, probabilistic technology provides a novel method to simulate biological, chemical, and neurobiological systems to reach previously unattainable simulation speeds and complexity. Additionally, many multi-media signal processing systems can take advantage of this computing approach to achieve tremendous gains in efficiency at the cost of imperceptible degradation in quality.

Probabilistic CMOS (PCMOS) allows a computing circuit to operate probabilistically and, as a result, achieve extreme power savings. A PCMOS circuit is a digital circuit where the supply voltage is lowered to sub-threshold levels; the output of the circuit is correct with some probability p < 1. The probabilistic nature of the computation may be artificially imposed or it may be a inevitable result of extreme semiconductor scaling. Furthermore, p can be precisely controlled using extant analog floating gate technology. This research uses PCMOS to perform computations and simulations that either require or can tolerate probabilistic behavior, specifically simulation of biological processes which can be extended to Monte Carlo simulations for any dynamical system. These methods allow orders-of-magnitude speed-up and substantially reduced power consumption. The research involves hardware, algorithmic, and theoretical aspects of probabilistic computing.