Several useful but computationally expensive sensor processing tasks can be mapped to the natural behavior of networks of ideal, passive analog components. For example, spatially smoothing an image can be achieved by convolving it with a Gaussian kernel, or by applying it to a 2-D resistor-capacitor network, and then relying on the diffusive behavior of the network to provide a smoothed image. Numerical computation is replaced with physical computation. But implementing analog networks is challenging due to the limitations of real analog components. They have low precision, vary with temperature, and are non-uniform from unit to unit. Moreover, physics limits the size of analog components. For example, to achieve a particular capacitance, using material of a given dielectric constant, a VLSI capacitor must occupy a certain area on the chip. Twice the capacitance will require twice the area. We describe a set of digital circuits that emulate analog components. These circuits provide analog behavior with arbitrary precision, uniformity, noise immunity, and no temperature dependence. Their size is limited by VLSI linewidths and the circuit approach taken. Networks of these digital circuits behave as do their analog equivalents, making physical computation practical for sensor processing closely coupled to the FPA.