  Reconfigurable accelerators for deep neural networks (DNNs) promise to
improve performance such as inference latency. STONNE is the first
cycle-accurate simulator for reconfigurable DNN inference accelerators which
allows for the exploration of accelerator designs and configuration space.
However, preparing models for evaluation and exploring configuration space in
STONNE is a manual developer-timeconsuming process, which is a barrier for
research. This paper introduces Bifrost, an end-to-end framework for the
evaluation and optimization of reconfigurable DNN inference accelerators.
Bifrost operates as a frontend for STONNE and leverages the TVM deep learning
compiler stack to parse models and automate offloading of accelerated
computations. We discuss Bifrost's advantages over STONNE and other tools, and
evaluate the MAERI and SIGMA architectures using Bifrost. Additionally, Bifrost
introduces a module leveraging AutoTVM to efficiently explore accelerator
designs and dataflow mapping space to optimize performance. This is
demonstrated by tuning the MAERI architecture and generating efficient dataflow
mappings for AlexNet, obtaining an average speedup of $50\times$ for the
convolutional layers and $11\times$ for the fully connected layers. Our code is
available at www.github.com/gicLAB/bifrost.
