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• W2757647500 abstract "The bottleneck between the processing elements and memory is the biggest issue contributing to the scalability problem in computing. In-memory computation is an alternative approach that combines memory and processor in the same location, and eliminates the potential memory bottlenecks. Associative processors are a promising candidate for in-memory computation, however the existing implementations have been deemed too costly and power hungry. Approximate computing is another promising approach for energy-efficient digital system designs where it sacrifices the accuracy for the sake of energy reduction and speedup in error-resilient applications. In this study, approximate in-memory computing is introduced in memristive associative processors. Two approximate computing methodologies are proposed; bit trimming and memristance scaling. Results show that the proposed methods not only reduce energy consumption of in-memory parallel computing but also improve their performance. As compared to other existing approximate computing methodologies on different architectures (e.g., CPU, GPU, and ASIC), approximate memristive in-memory computing exhibits better results in terms of energy reduction (up to 80x) and speedup (up to 20x) on a variety of benchmarks from different domains when quality degradation is limited to 10% and it confirms that memristive associative processors provide a highly-promising platform for approximate computing." @default.
• W2757647500 created "2017-10-06" @default.
• W2757647500 creator A5008034875 @default.
• W2757647500 creator A5012920132 @default.
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• W2757647500 date "2017-09-27" @default.
• W2757647500 modified "2023-09-25" @default.
• W2757647500 title "Approximate Memristive In-memory Computing" @default.
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• W2757647500 doi "https://doi.org/10.1145/3126526" @default.
• W2757647500 hasPublicationYear "2017" @default.
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