Abstract
Recently, threshold logic attracts a lot of attention due to the advances of its physical implementation and the strong binding to neural networks.On the other hand, approximate computing is a new design paradigm that focuses on error-tolerant applications, e.g., machine learning or pattern recognition.In this thesis, we integrate threshold logic with approximate computing and propose a synthesis algorithm to obtain cost-efficient approximate threshold logic circuits with an error rate guarantee.We conduct experiments on a set of IWLS 2005 benchmarks.The experimental results show that the proposed algorithm can efficiently explore the approximability of each benchmark.For a 5\% error rate constraint, the circuit cost can be reduced by 22.8\% on average.