  The computational complexity of leveraging deep neural networks for
extracting deep feature representations is a significant barrier to its
widespread adoption, particularly for use in embedded devices. One particularly
promising strategy to addressing the complexity issue is the notion of
evolutionary synthesis of deep neural networks, which was demonstrated to
successfully produce highly efficient deep neural networks while retaining
modeling performance. Here, we further extend upon the evolutionary synthesis
strategy for achieving efficient feature extraction via the introduction of a
stress-induced evolutionary synthesis framework, where stress signals are
imposed upon the synapses of a deep neural network during training to induce
stress and steer the synthesis process towards the production of more efficient
deep neural networks over successive generations and improved model fidelity at
a greater efficiency. The proposed stress-induced evolutionary synthesis
approach is evaluated on a variety of different deep neural network
architectures (LeNet5, AlexNet, and YOLOv2) on different tasks (object
classification and object detection) to synthesize efficient StressedNets over
multiple generations. Experimental results demonstrate the efficacy of the
proposed framework to synthesize StressedNets with significant improvement in
network architecture efficiency (e.g., 40x for AlexNet and 33x for YOLOv2) and
speed improvements (e.g., 5.5x inference speed-up for YOLOv2 on an Nvidia Tegra
X1 mobile processor).
