  With the popularity of the deep neural network (DNN), hardware accelerators
are demanded for real time execution. However, lengthy design process and fast
evolving DNN models make hardware evaluation hard to meet the time to market
need. This paper proposes a pre-RTL DNN hardware evaluator that supports
conventional layer-by-layer processing as well as the fused layer processing
for low external bandwidth requirement. The evaluator supports two
state-of-the-art accelerator architectures and finds the best hardware and
layer fusion group The experimental results show the layer fusion scheme can
achieve 55.6% memory bandwidth reduction, 36.7% latency improvement and 49.2%
energy reduction compared with layer-by-layer operation.
