In this work, we open the black box of the digital neuromorphic processor for algorithm designers by presenting the neuron processing instruction set and detailed energy consumption of SENeCA neuromorphic architecture. For convenient benchmarking and optimization, we provide energy cost of essential neuromorphic components in SENeCA, including neuron models and learning rules. We show the energy efficiency of SNN algorithms for video processing and online learning, and demonstrate the potential of our work for optimizing algorithm designs. Overall, we present a practical approach to enable algorithm designers to accurately benchmark SNN algorithms and pave the way towards effective algorithm-hardware co-design.