SS-MRAM: A Segment-Based Search Scheme With Configurable Matching for High-Accuracy Hyperdimensional Computing in CAM Applications

With the rise of hyperdimensional computing (HDC), content-addressable memories (CAMs) have emerged as an ideal choice for processing high-dimensional data. However, despite the advantages of high parallelism and low latency offered by CAM technology, it fails to address the significant loss of inference accuracy caused by closely matching hamming distances (HD). Emerging analog-based imprecise in-memory computing technologies frequently provide a minimum detectable HD that is insufficient for meeting the requirements of high similarity tasks. This limitation provides opportunities for using digital methods to realize fully exact matching memory computing technology based on magnetic random access memory (MRAM). In this work, a segment search scheme based on STT-MRAM devices and address-matching technology is proposed, which achieves zero loss in inference accuracy. An adaptive amplification structure is initially implemented by integrating the discharge method of latch structures, accompanied by the design of a 14T-2MTJ cell circuit. Through the optimization of the matching step, HD are calculated for each segment of configurable-dimensional vectors, ultimately facilitating the classification ranking of query hypervectors based on a comprehensive array architecture. Experimental results indicate that there is zero loss in inference accuracy when each segment of the dimension is configured to be below 16 bits. At 16 bits, the search power consumption in the worst-case matching scenario is measured at 1.73 fJ/bit, while the loss in inference accuracy does not exceed 0.4%.