  Systolic arrays are a promising computing concept which is in particular
inline with CMOS technology trends and linear algebra operations found in the
processing of artificial neural networks. The recent success of such deep
learning methods in a wide set of applications has led to a variety of models,
which albeit conceptual similar as based on convolutions and fully-connected
layers, in detail show a huge diversity in operations due to a large design
space: An operand's dimension varies substantially since it depends on design
principles such as receptive field size, number of features, striding, dilating
and grouping of features. Last, recent networks extent previously plain
feedforward models by various connectivity, such as in ResNet or DenseNet. The
problem of choosing an optimal systolic array configuration cannot be solved
analytically, thus instead methods and tools are required that facilitate a
fast and accurate reasoning about optimality in terms of total cycles,
utilization, and amount of data movements. In this work we introduce Camuy, a
lightweight model of a weight-stationary systolic array for linear algebra
operations that allows quick explorations of different configurations, such as
systolic array dimensions and input/output bitwidths. Camuy aids accelerator
designers in either finding optimal configurations for a particular network
architecture or for robust performance across a variety of network
architectures. It offers simple integration into existing machine learning tool
stacks (e.g TensorFlow) through custom operators. We present an analysis of
popular DNN models to illustrate how it can estimate required cycles, data
movement costs, as well as systolic array utilization, and show how the
progress in network architecture design impacts the efficiency of inference on
accelerators based on systolic arrays.
