  The design of specialized architectures for accelerating the inference
procedure of Deep Neural Networks (DNNs) is a booming area of research
nowadays. First-generation rigid proposals have been rapidly replaced by more
advanced flexible accelerator architectures able to efficiently support a
variety of layer types and dimensions. As the complexity of the designs grows,
it is more and more appealing for researchers to have cycle-accurate simulation
tools at their disposal to allow for fast and accurate design-space
exploration, and rapid quantification of the efficacy of architectural
enhancements during the early stages of a design. To this end, we present
STONNE (Simulation TOol of Neural Network Engines), a cycle-accurate,
highly-modular and highly-extensible simulation framework that enables
end-to-end evaluation of flexible accelerator architectures running complete
contemporary DNN models. We use STONNE to model the recently proposed MAERI
architecture and show how it can closely approach the performance results of
the publicly available BSV-coded MAERI implementation. Then, we conduct a
comprehensive evaluation and demonstrate that the folding strategy implemented
for MAERI results in very low compute unit utilization (25% on average across 5
DNN models) which in the end translates into poor performance.
