Design of an Energy Efficient Voltage-to-Time Converter with Rectified Linear Unit Characteristics for Artificial Neural Networks

Machine learning at the edge offers fast, secure and intelligent signal processing. However, calculations need to be very energy efficient because of the limited power budget. This paper presents the design of an energy efficient voltage-to-time converter circuit in 22 nm FD-SOI CMOS technology. The circuit has a rectified linear unit transfer characteristic and is therefore well suited for analog mixed signal computing architectures for artificial neural networks at the edge. Depending on whether mismatch is compensated or not, the effective resolution for a maximum pulse length of 430 ps is 3.0 b or 6.4 b. The simulated energy consumption is below 3 fJ for every output pulse.