  Recent studies on automatic neural architectures search have demonstrated
significant performance, competitive to or even better than hand-crafted neural
architectures. However, most of the existing network architecture tend to use
residual, parallel structures and concatenation block between shallow and deep
features to construct a large network. This requires large amounts of memory
for storing both weights and feature maps. This is challenging for mobile and
embedded devices since they may not have enough memory to perform inference
with the designed large network model. To close this gap, we propose MemNet, an
augment-trim learning-based neural network search framework that optimizes not
only performance but also memory requirement. Specifically, it employs memory
consumption based ranking score which forces an upper bound on memory
consumption for navigating the search process. Experiment results show that, as
compared to the state-of-the-art efficient designing methods, MemNet can find
an architecture which can achieve competitive accuracy and save an average of
24.17% on the total memory needed.

Machine Learning;Computer Vision and Pattern Recognition;Machine Learning