The design of wearable systems comes with constraints in computational and power resources. We describe the development of customized hardware for the wearable discrimination of human sleep and wake based on cardio-respiratory signals. The device was designed for efficient and low-power computation of Fast Fourier Transforms and artificial neural networks required for the on-line classification. We discuss methods for reducing computational load and consequently power requirements of the device. The developed wearable SleePic prototype was tested for autonomy and comfort on eight healthy subjects. SleePic showed an energetic autonomy of more than 36 hours. The SleePic device will require further integration for increased comfort and improved user interaction.