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Approximate Logic Synthesis from Examples

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For inherently error resilient applications, it is possible to decrease the computing accuracy to achieve higher efficiency in terms of area, performance, and energy. Such applications are abundant in many domains, including signal processing, robotic, and machine learning. The process of automatically synthesizing an approximate circuit from a design description is called Approximate Logic Synthesis (ALS). In this regard, we investigate two research threads: 1) ALS where the circuit structure is known; 2) ALS where the structure is unknown. In this talk, I will introduce our recent progress on approximate logic synthesis from examples, a form of supervised learning of Boolean functions.

This talk is part of the CAS Talks series.

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