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In-Memory Computing Architectures for Sparse Distributed Memory
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DOI：10.1109/TBCAS.2016.2545402.png)
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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 x and 12 x delay and energy reduction, respectively, over conventional SDM. When classifying 16 x16 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) <= 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.
Keyword:
Associative memory
brain-inspired computing
Compute Memory
machine learning
pattern recognition
sparse distributed memory
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