  As a type of prominent studies in deep learning, generative models have been
widely investigated in research recently. Two research branches of the deep
learning models, the Generative Networks (GANs, VAE) and the Semantic
Segmentation, rely highly on the upsampling operations, especially the
transposed convolution and the dilated convolution. However, these two types of
convolutions are intrinsically different from standard convolution regarding
the insertion of zeros in input feature maps or in kernels respectively. This
distinct nature severely degrades the performance of the existing deep learning
engine or frameworks, such as Darknet, Tensorflow, and PyTorch, which are
mainly developed for the standard convolution. Another trend in deep learning
realm is to deploy the model onto edge/ embedded devices, in which the memory
resource is scarce. In this work, we propose a Highly Untangled
Generative-model Engine for Edge-computing or HUGE2 for accelerating these two
special convolutions on the edge-computing platform by decomposing the kernels
and untangling these smaller convolutions by performing basic matrix
multiplications. The methods we propose use much smaller memory footprint,
hence much fewer memory accesses, and the data access patterns also
dramatically increase the reusability of the data already fetched in caches,
hence increasing the localities of caches. Our engine achieves a speedup of
nearly 5x on embedded CPUs, and around 10x on embedded GPUs, and more than 50%
reduction of memory access.
