Parasitic Resistance Effect Analysis in RRAM-based TCAM for Memory Augmented Neural Networks
2020 IEEE International Memory Workshop (IMW)(2020)
摘要
Memory augmented neural networks (MANNs) enable trained neural networks to rapidly learn new classes from few examples. However, content-based addressing is inefficient in conventional computer system due to the von Neumann bottleneck. Ternary content-addressable memories (TCAMs) based on resistive random access memory (RRAM) provide a promising approach to accelerate the addressing according to the Hamming distances (HDs) between the search vector and stored vectors. Generally, the HD is sensed from the discharge rate of a match line, exhibiting a linear dependence on the number of mismatched bits. However, parasitic resistance effect causes that the location of mismatched bits also determines the HD. This work proposes a compact model to evaluate the discharge rate of a match line. The impact of parasitic resistance effect in RRAM- based TCAMs is analyzed. Parasitic resistance effect is also incorporated into MANNs during few-shot learning. Remarkable accuracy losses are observed as parasitic line resistance and columns of TCAM increase. Our analyses provide valuable design guidelines of RRAM-based TCAM for future MANN systems.
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关键词
Parasitic resistance,RRAM,TCAM,MANN,few- shot learning
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