Non-idealities and Design Solutions for Analog Memristor-Based Content-Addressable Memories.

Memristor-based analog Content Addressable Memories (aCAMs) offer robust parallel pattern look-up capabilities, significantly enhancing the scope of In-Memory Computing applications. This paper presents challenges of these analog circuits, which may occur during the inference, and proposes solutions to overcome them. Precisely, we investigate the impact of temperature-dependent behavior, CMOS process variations and memristor telegraph read noise. We demonstrate that one challenging issue affecting memristors analog computing applications, namely telegraph read noise, is not a significant problem in aCAM. We introduce a framework that accounts for these combined distortions to define variability-aware aCAM windows and estimate the bit resolution of a CAM cell. Using this framework we estimate the bit resolution to 2 bits before applying compensating measures and to 4 bits afterwards. We study how variations affect the inference accuracy of the IRIS classification dataset using our novel torchCAM model. We introduce a streamlined aCAM design featuring a memristor comparator for simplified input-to-reference comparison and a novel cell architecture with two symmetrical memristor comparator units.