  Systolic Arrays are one of the most popular compute substrates within Deep
Learning accelerators today, as they provide extremely high efficiency for
running dense matrix multiplications. However, the research community lacks
tools to insights on both the design trade-offs and efficient mapping
strategies for systolic-array based accelerators. We introduce Systolic CNN
Accelerator Simulator (SCALE-Sim), which is a configurable systolic array based
cycle accurate DNN accelerator simulator. SCALE-Sim exposes various
micro-architectural features as well as system integration parameters to the
designer to enable comprehensive design space exploration. This is the first
systolic-array simulator tuned for running DNNs to the best of our knowledge.
Using SCALE-Sim, we conduct a suite of case studies and demonstrate the effect
of bandwidth, data flow and aspect ratio on the overall runtime and energy of
Deep Learning kernels across vision, speech, text, and games. We believe that
these insights will be highly beneficial to architects and ML practitioners.
