  Digital Pre-Distortion (DPD) enhances signal quality in wideband RF power
amplifiers (PAs). As signal bandwidths expand in modern radio systems, DPD's
energy consumption increasingly impacts overall system efficiency. Deep Neural
Networks (DNNs) offer promising advancements in DPD, yet their high complexity
hinders their practical deployment. This paper introduces open-source
mixed-precision (MP) neural networks that employ quantized low-precision
fixed-point parameters for energy-efficient DPD. This approach reduces
computational complexity and memory footprint, thereby lowering power
consumption without compromising linearization efficacy. Applied to a 160MHz-BW
1024-QAM OFDM signal from a digital RF PA, MP-DPD gives no performance loss
against 32-bit floating-point precision DPDs, while achieving -43.75 (L)/-45.27
(R) dBc in Adjacent Channel Power Ratio (ACPR) and -38.72 dB in Error Vector
Magnitude (EVM). A 16-bit fixed-point-precision MP-DPD enables a 2.8X reduction
in estimated inference power. The PyTorch learning and testing code is publicly
available at \url{https://github.com/lab-emi/OpenDPD}.
