Abstract: Examples increase precision for aCAMs by converting an input signal (x) received by a circuit into a first analog voltage signal (V(xMSB)) representing the most significant bits of the input signal (x) and a second analog voltage signal (V(xLSB)) representing the least significant bits of the input signal (x). By dividing the input signal (x) bit-wise into the first analog voltage signal (V(xMSB)) and the second analog voltage signal (V(xLSB)), the circuit can utilize aCAM sub-circuits implementing a combination of Boolean operations to search the input signal (x) against 22*M programmable levels, where “M” represents the number of programmable bits for each aCAM sub-circuit. Thus, using similar circuit hardware, example circuits square the number of programmable levels of conventional aCAMs (which generally only have 2M programmable levels). Accordingly, examples provide new aCAMs that can carry out more complex computations than conventional aCAMs of comparable cost, size, and power consumption.

Abstract: Systems and methods provide new circuits that increase aCAM precision by leveraging the concept of range segmenting to representationally store an analog voltage range across multiple aCAM cells/sub-circuits (here the representationally stored analog voltage range may correspond to a word entry). In this way, a circuit of the presently disclosed technology can increase precision (e.g., the number of programmable levels that can be used to store a word entry and/or the number of programmable levels that an input signal can be search against) linearly with each aCAM cell/sub-circuit added to the circuit. Accordingly, circuits of the presently disclosed technology can be used to carry out more complex computations than conventional aCAMs—and thus can be used in a wider range of computational applications.