  DNN/Accelerator co-design has shown great potential in improving QoR and
performance. Typical approaches separate the design flow into two-stage: (1)
designing an application-specific DNN model with high accuracy; (2) building an
accelerator considering the DNN specific characteristics. However, it may fail
in promising the highest composite score which combines the goals of accuracy
and other hardware-related constraints (e.g., latency, energy efficiency) when
building a specific neural-network-based system. In this work, we present a
single-stage automated framework, YOSO, aiming to generate the optimal solution
of software-and-hardware that flexibly balances between the goal of accuracy,
power, and QoS. Compared with the two-stage method on the baseline systolic
array accelerator and Cifar10 dataset, we achieve 1.42x~2.29x energy or
1.79x~3.07x latency reduction at the same level of precision, for different
user-specified energy and latency optimization constraints, respectively.
