  Generative neural network is a new category of neural networks and it has
been widely utilized in applications such as content generation, unsupervised
learning, segmentation and pose estimation. It typically involves massive
computing-intensive deconvolution operations that cannot be fitted to
conventional neural network processors directly. However, prior works mainly
investigated specialized hardware architectures through intensive hardware
modifications to the existing deep learning processors to accelerate
deconvolution together with the convolution. In contrast, this work proposes a
novel deconvolution implementation with a software approach and enables fast
and efficient deconvolution execution on the legacy deep learning processors.
Our proposed method reorganizes the computation of deconvolution and allows the
deep learning processors to treat it as the standard convolution by splitting
the original deconvolution filters into multiple small filters. Compared to
prior acceleration schemes, the implemented acceleration scheme achieves 2.41x
- 4.34x performance speedup and reduces the energy consumption by 27.7% - 54.5%
on a set of realistic benchmarks. In addition, we also applied the
deconvolution computing approach to the off-the-shelf commodity deep learning
processors. The performance of deconvolution also exhibits significant
performance speedup over prior deconvolution implementations.
