As long as a computational precision above 8 bits is preferred, digital design generally outperforms analog one incurring less hardware cost. This motivates our recent studies on digital approximate computing as presented in this paper. Rather than using fixed-point numbers, discrete steps of approximation using floating-point number representations such as BFloat16 and posit formats are explored particularly. Time-domain computing that starts in the digital domain with discrete delay values towards analog domain under increased delay uncertainties when pushed by voltage scaling for energy efficiency is addressed as well. The proposed approximate arithmetic and nonlinear activation functions are further evaluated in various artificial neural networks achieving competitive Quality-of-Service compared to the state-of-the-art with full-precision computing.