We present a design of a majority neuron circuit with non-volatile synaptic weights. It is based on an analog majority circuit composed of controlled current inverters (CCIs). The proposed circuit is immune to device parameter fluctuations, and its fan-in is estimated about 1000. Synaptic weights are realized on the neuron circuit by adding variable resistors. We consider a design of a non-volatile synaptic weight by using a three-terminal magnetic domain-wall motion (DWM) device. The operation of a fully connected recurrent neural network composed of the proposed circuits has been confirmed by SPICE simulation.

In area-based stereo matching, there is a problem called "boundary overreach", i.e. the recovered object boundary turns out to be wrongly located away from the real one. This is especially harmful to segmenting objects using depth information. A few approaches have been proposed to solve this problem. However, these techniques tend to degrade on smooth surfaces. That is, there seems to be a trade-off problem between recovering precise object edges and obtaining smooth surfaces. In this paper, we propose a new simple method to solve this problem. Using multiple stereo pairs and multiple windowing, our method detects the region where the boundary overreach is likely to occur (let us call it "BO region") and adopts appropriate methods for the BO and non-BO regions. Although the proposed method is quite simple, the experimental results have shown that it is very effective at recovering both sharp object edges at their correct locations and smooth object surfaces.