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Recent advances in machine learning and neuro-inspired systems enabled theincreased interest in efficient pattern recognition at the edge. A wide varietyof applications, such as near-sensor classification, require fast and low-powerapproaches for pattern matching through the use of associative memories andtheir more well-known implementation, Content Addressable Memories (CAMs).Towards addressing the need for low-power classification, this work showcasesan RRAM-based analogue CAM (ACAM) intended for template matching applications,providing a low-power reconfigurable classification engine for the extremeedge. The circuit uses a low component count at 6T2R2M, comparable with themost compact existing cells of this type. In this work, we demonstrate ahardware prototype, built with commercial off-the-shelf (COTS) components forthe MOSFET-based circuits, that implements rows of 6T2R2M employing TiOx-basedRRAM devices developed in-house, showcasing competitive matching windowconfigurability and definition. Furthermore, through simulations, we validatethe performance of the proposed circuit by using a commercially available 180nmtechnology and in-house RRAM data-driven model to assess the energydissipation, exhibiting 60 pJ per classification event.

An RRAM-Based Implementation of a Template Matching Circuit for Low-Power Analogue Classification