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Sensor-based Approximate Adder Design For Accelerating Error-tolerant And Deep-learning Applications

SENSOR-BASED APPROXIMATE ADDER DESIGN FOR ACCELERATING ERROR-TOLERANT AND DEEP-LEARNING APPLICATIONS

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  • TVMAFE76
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SENSOR-BASED APPROXIMATE ADDER DESIGN FOR ACCELERATING ERROR-TOLERANT AND DEEP-LEARNING APPLICATIONS

Approximate computing is an emerging strategy which trades computational accuracy for computational cost in terms of performance, energy, and/or area. In this paper, we propose a novel sensor-based approximate adder for highperformance energy-efficient arithmetic computation, while considering the accuracy requirement of error-tolerant applications. This is the first work using in-situ sensors for approximate adder design, based on monitoring online transition activity on the carry chain and speculating on carry propagation/truncation. On top of a fully-optimized ripple-carry adder, the performance of our adder is enhanced by 2.17X. When applied in error-tolerant applications such as image processing and handwritten digit recognition, our approximate adder leads to very promising quality of results compared to the case when an accurate adder is used.

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