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引用次数: 0
摘要
超维关联内存(HAM)的低功耗和低延迟提高了超维计算(HDC)的效率。然而,由于 HAM 需要存储所有类超向量,并完成它们与输入样本超向量之间的相似性计算,因此很难进一步降低 HAM 的开销。为了解决这个问题,我们提出了一种名为 AttnACQ 的基于关注-自相关的查询方法,以避免类超向量的存储和操作。此外,利用 SOT-MRAM 的超低读写延迟,提出了一种与 AttnACQ 匹配的高效 HAM。实验表明,这种 AttnACQ 增强型 HAM 可节省 95% 以上的面积和 50% 的延迟。
AttnACQ: Attentioned-AutoCorrelation-Based Query for Hyperdimensional Associative Memory
The low power and latency of hyperdimensional associative memory (HAM) promotes hyperdimensional computing (HDC)’s efficiency. However, overheads of HAM can be hardly further reduced, since HAM needs to store all class hypervectors, and complete the similarity calculation between them and the input sample hypervector. To address this issue, an attentioned-autocorrelation based query method called AttnACQ is proposed to avoid the storage and operations corresponding to class hypervectors. Furthermore, levearging SOT-MRAM’s ultra-low read/write delay, a high-efficient HAM matching to AttnACQ is proposed. Experiments show that this AttnACQ boosted HAM saves more than 95% area and 50% latency.
期刊介绍:
TCAS II publishes brief papers in the field specified by the theory, analysis, design, and practical implementations of circuits, and the application of circuit techniques to systems and to signal processing. Included is the whole spectrum from basic scientific theory to industrial applications. The field of interest covered includes:
Circuits: Analog, Digital and Mixed Signal Circuits and Systems
Nonlinear Circuits and Systems, Integrated Sensors, MEMS and Systems on Chip, Nanoscale Circuits and Systems, Optoelectronic
Circuits and Systems, Power Electronics and Systems
Software for Analog-and-Logic Circuits and Systems
Control aspects of Circuits and Systems.