Real-Time Reconfigurable Linear Threshold Elements and Some Applications to Neural Hardware

Abstract. This paper discusses some aspects regarding the use of uni-versal linear threshold elements implemented in a standard double-poly CMOS technology, which might be used for neural networks as well as plain, or mixed-signal, analog and digital circuits. The 2-transistor ele-ments can have their threshold adjusted in real time, and thus the basic Boolean function, by changing the voltage on one or more of the in-puts. The proposed elements allow for significant reduction in transistor count and number of interconnections. This in combination with a power supply voltage in the range of less than 100 mV up to typically 1.0 V allow for Power-Delay-product improvements typically in the range of hundreds to thousands of times compared to standard implementations in a 0.6 micron CMOS technology. This makes the circuits more sim-ilar to biological neurons than most existing CMOS implementations. Circuit examples are explored by theory, SPICE simulations and chip measurements. A way of exploiting inherit fault tolerance is briefly men-tioned. Potential improvements on operational speed and chip area of linear threshold elements used for perceptual tasks are shown.