  The acceleration of CNNs has gained increasing atten-tion since their success
in computer vision. With the heterogeneous functional layers that cannot be
pro-cessed by the accelerators proposed for convolution layers only, modern
end-to-end CNN acceleration so-lutions either transform the diverse computation
into matrix/vector arithmetic, which loses data reuse op-portunities in
convolution, or introduce dedicated functional unit to each kind of layer,
which results in underutilization and high update expense. To enhance the
whole-life cost efficiency, we need an acceleration solution that is efficient
in processing CNN layers and has the generality to apply to all kinds of
existing and emerging layers. To this end, we pro-pose GCONV Chain, a method to
convert the entire CNN computation into a chain of standard general
convolutions (GCONV) that can be efficiently pro-cessed by the existing CNN
accelerators. This paper comprehensively analyzes the GCONV Chain model and
proposes a full-stack implementation to support GCONV Chain. On one hand, the
results on seven var-ious CNNs demonstrate that GCONV Chain improves the
performance and energy efficiency of existing CNN accelerators by an average of
3.4x and 3.2x re-spectively. On the other hand, we show that GCONV Chain
provides low whole-life costs for CNN accelera-tion, including both developer
efforts and total cost of ownership for the users.
