Specifically, we believe that VLSI technology permits computation to be an integral part of most engineering artifacts. This frees us from the constraints of the performance of conventional electromechanical systems and permits the construction of systems whose performance is determined by the results of computation. This methodology allows us to construct systems that directly implement theoretical results. In a broad sense, it is our intent to close the long-standing gap between theory and engineering practice, specifically through the use of VLSI computational constructs.
This will become a pervasive methodology because systems which make use of active engineering mathematics in the form of VLSI circuits will be very much more cost-effective than conventional systems. Ultimately, this form of engineering will provide cost-effective solutions to the most challenging problems in information engineering: problems that we cannot even begin to solve today.
Examples of results of this program are:
This work has been further developed as described in "Reset Noise Reduction in Capacitive Sensors," Boyd Fowler, Michael D. Godfrey, and Steve Mims, IEEE TCAS-1, vol.53, No. 8, August 2006. The abstract from this paper reads:
Reset noise sets a fundamental detection limit on capacitive sensors. Many sensing circuits depend on accumulating charge on a capacitor as the sensing method. Reset noise is the noise that occurs when the capacitor is reset prior to the charge accumulation cycle. Therefore, it is important to understand the factors which determine reset noise, and how this noise may be mitigated. The purpose of this paper is to show how capacitive reset noise can be reduced during the reset cycle. We present and analyze three circuits that implement the basic methods for directly reducing capacitive reset noise. In addition, we present a time-domain technique for analyzing the time-varying statistics of these circuits. This technique makes use of Ito calculus to obtain solutions to the time-varying stochastic differential equations. Theoretical noise calculations and Monte Carlo simulation results are presented for each technique. We show that theory and simulation yield similar results.Finally, we show in the examples that reset noise may be reduced by a factor of 20 or more. We also refer to implemented sensor arrays which achieve these results.
The origins of this work were available on the web page of the Physics of Computation Group at Caltech, but this page is no longer active. If you are interested, take a look at other places where these ideas have been pursued, such as John Lazzaro's page where you will find the Chipmunk analog and digital VLSI design tools. Tobi Delbruck organized the Caltech Physics of Computation home page, which contained much interesting material. Tobi is now at INI in Zurich, and Carver Mead retired from Caltech in 2001. A good place to find out more these days is the Institute for Neuroinformatics at ETH/University of Zurich.
Or, if you are interested in other work that I have been involved in, such as statistics, mathematical economics, or computing look at other research.