  We implement a differentiable Neural Architecture Search (NAS) method
inspired by FBNet for discovering neural networks that are heavily optimized
for a particular target device. The FBNet NAS method discovers a neural network
from a given search space by optimizing over a loss function which accounts for
accuracy and target device latency. We extend this loss function by adding an
energy term. This will potentially enhance the ``hardware awareness" and help
us find a neural network architecture that is optimal in terms of accuracy,
latency and energy consumption, given a target device (Raspberry Pi in our
case). We name our trained child architecture obtained at the end of search
process as Hardware Aware Neural Network Architecture (HANNA). We prove the
efficacy of our approach by benchmarking HANNA against two other
state-of-the-art neural networks designed for mobile/embedded applications,
namely MobileNetv2 and CondenseNet for CIFAR-10 dataset. Our results show that
HANNA provides a speedup of about 2.5x and 1.7x, and reduces energy consumption
by 3.8x and 2x compared to MobileNetv2 and CondenseNet respectively. HANNA is
able to provide such significant speedup and energy efficiency benefits over
the state-of-the-art baselines at the cost of a tolerable 4-5% drop in
accuracy.
