  With the popularity of deep learning, the hardware implementation platform of
deep learning has received increasing interest. Unlike the general purpose
devices, e.g., CPU, or GPU, where the deep learning algorithms are executed at
the software level, neural network hardware accelerators directly execute the
algorithms to achieve higher both energy efficiency and performance
improvements. However, as the deep learning algorithms evolve frequently, the
engineering effort and cost of designing the hardware accelerators are greatly
increased. To improve the design quality while saving the cost, design
automation for neural network accelerators was proposed, where design space
exploration algorithms are used to automatically search the optimized
accelerator design within a design space. Nevertheless, the increasing
complexity of the neural network accelerators brings the increasing dimensions
to the design space. As a result, the previous design space exploration
algorithms are no longer effective enough to find an optimized design. In this
work, we propose a neural network accelerator design automation framework named
GANDSE, where we rethink the problem of design space exploration, and propose a
novel approach based on the generative adversarial network (GAN) to support an
optimized exploration for high dimension large design space. The experiments
show that GANDSE is able to find the more optimized designs in negligible time
compared with approaches including multilayer perceptron and deep reinforcement
learning.
