  Neural architecture search (NAS) has been very successful at outperforming
human-designed convolutional neural networks (CNN) in accuracy, and when
hardware information is present, latency as well. However, NAS-designed CNNs
typically have a complicated topology, therefore, it may be difficult to design
a custom hardware (HW) accelerator for such CNNs. We automate HW-CNN codesign
using NAS by including parameters from both the CNN model and the HW
accelerator, and we jointly search for the best model-accelerator pair that
boosts accuracy and efficiency. We call this Codesign-NAS. In this paper we
focus on defining the Codesign-NAS multiobjective optimization problem,
demonstrating its effectiveness, and exploring different ways of navigating the
codesign search space. For CIFAR-10 image classification, we enumerate close to
4 billion model-accelerator pairs, and find the Pareto frontier within that
large search space. This allows us to evaluate three different
reinforcement-learning-based search strategies. Finally, compared to ResNet on
its most optimal HW accelerator from within our HW design space, we improve on
CIFAR-100 classification accuracy by 1.3% while simultaneously increasing
performance/area by 41% in just~1000 GPU-hours of running Codesign-NAS.
