Approximate Memristive In-memory Computing

作者：Yantir Hasan Erdem^*; Eltawil Ahmed M; Kurdahi Fadi J

来源：ACM Transactions on Embedded Computing Systems, 2017, 16(5s): 129.

DOI：10.1145/3126526

• Memristor
• in-memory computing
• memristive associative processor (MAP)
• approximate computing
• memristance scaling
• approximate storage
• architecture

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.

• 出版日期2017-10