  Spiking Neural Networks (SNN) represent a biologically inspired computation
model capable of emulating neural computation in human brain and brain-like
structures. The main promise is very low energy consumption. Unfortunately,
classic Von Neumann architecture based SNN accelerators often fail to address
demanding computation and data transfer requirements efficiently at scale. In
this work, we propose a promising alternative, an in-memory SNN accelerator
based on Spintronic Computational RAM (CRAM) to overcome scalability
limitations, which can reduce the energy consumption by up to 164.1$\times$
when compared to a representative ASIC solution.
