This paper presents an energy-efficient and high-throughput architecture for Sparse Distributed Memory (SDM)—a computational model of the human brain [1]. The proposed SDM architecture is based on the recently proposed in-memory computing kernel for machine learning applications called  Compute Memory  (CM) [2], [3]. CM achieves energy and throughput efficiencies by deeply embedding computation into the memory array. SDM-specific techniques such as hierarchical binary decision (HBD) are employed to reduce the delay and energy further. The CM-based SDM (CM-SDM) is a mixed-signal circuit, and hence circuit-aware behavioral, energy, and delay models in a 65 nm CMOS process are developed in order to predict system performance of SDM architectures in the auto- and hetero-associative modes. The delay and energy models indicate that CM-SDM, in general, can achieve up to 25    $\times$   and 12    $\times$   delay and energy reduction, respectively, over conventional SDM. When classifying 16    $\times$   16 binary images with high noise levels (input bad pixel ratios: 15%–25%) into nine classes, all SDM architectures are able to generate output bad pixel ratios    $(B_{o})\leq 2\%$  . The CM-SDM exhibits negligible loss in accuracy, i.e., its    $B_{o}$   degradation is within 0.4% as compared to that of the conventional SDM.
['Mingu Kang', 'Naresh R. Shanbhag']
In-Memory Computing Architectures for Sparse Distributed Memory