  Sparse and event-driven spiking neural network (SNN) algorithms are the ideal
candidate solution for energy-efficient edge computing. Yet, with the growing
complexity of SNN algorithms, it isn't easy to properly benchmark and optimize
their computational cost without hardware in the loop. Although digital
neuromorphic processors have been widely adopted to benchmark SNN algorithms,
their black-box nature is problematic for algorithm-hardware co-optimization.
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 the SENeCA neuromorphic architecture. For
convenient benchmarking and optimization, we provide the energy cost of the
essential neuromorphic components in SENeCA, including neuron models and
learning rules. Moreover, we exploit the SENeCA's hierarchical memory and
exhibit an advantage over existing neuromorphic processors. 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.
