Breaking the Scalability Barrier of Content Addressable Memories: A Probabilistic Alternative for Large-Key Associative Search

Content Addressable Memories (CAMs) offer high-speed, deterministic lookups but face significant scalability challenges with large input keys ( \(>\) 100 bits), leading to excessive power, silicon area, and memory costs. This paper introduces Probabilistic Content Addressable Memory (P-CAM), a novel architecture designed to overcome these limitations by trading strict determinism for memory efficiency and scalability. P-CAM compresses high-dimensional inputs into fixed-size fingerprints using hashing, making memory requirements independent of key length. P-CAM preserves the constant-time lookup advantage of CAMs, while supporting applications with large keys, such as networking, bioinformatics, and machine learning, where conventional CAMs are impractical. FPGA implementation on Xilinx UltraScale+ devices shows that P-CAM maintains constant query latency and delivers 15 \(\times\) improvement in resource efficiency when handling 384-bit keys, compared to state-of-the-art deterministic CAMs designed for narrower inputs. Although P-CAM's probabilistic nature introduces a small, controllable false-positive rate, it can be configured for fully deterministic operation under specific constraints. To the best of our knowledge, P-CAM is the first CAM architecture to employ a fingerprint-based probabilistic data structure as the primary storage mechanism for associative lookup, distinguishing it from prior probabilistic approaches that are limited to set membership checks, offering a robust and scalable alternative for modern data-intensive systems.