Traditional approaches to calculating self-motion from visual information in artificial devices have generally relied on object identification and/or correlation of image sections between successive frames. Such calculations are computationally expensive and real-time digital implementation requires powerful processors. In contrast flies arrive at essentially the same outcome, the estimation of self-motion, in a much smaller package using vastly less power. Despite the potential advantages and a few notable successes, few neuromorphic analog VLSI devices based on biological vision have been employed in practical applications to date. This paper describes a hardware implementation in aVLSI of our recently developed adaptive model for motion detection. The chip integrates motion over a linear array of local motion processors to give a single voltage output. Although the device lacks on-chip photodetectors, it includes bias circuits to use currents from external photodiodes, and we have integrated it with a ring-array of 40 photodiodes to form a visual rotation sensor. The ring configuration reduces pattern noise and combined with the pixel-wise adaptive characteristic of the underlying circuitry, permits a robust output that is proportional to image rotational velocity over a large range of speeds, and is largely independent of either mean luminance or the spatial structure of the image viewed. In principle, such devices could be used as an element of a velocity-based servo to replace or augment inertial guidance systems in applications such as mUAVs.