Machine learning at the edge is fast, secure and robust. Because of the limited power budget, calculations need to be very energy efficient. This paper presents the design of an energy efficient voltage-to-time converter circuit in 22nm FD-SOI CMOS technology. It has a rectified linear unit transfer characteristic and is suited for analog mixed signal computing architectures for artificial neural network inference. Depending on whether a calibration process is performed or not, the resolution for a maximum pulse width of 430 ps is 3.0 b or 6.4 b. The energy consumption per cycle stays below 3 fJ.