  Many DNN-enabled vision applications constantly operate under severe energy
constraints such as unmanned aerial vehicles, Augmented Reality headsets, and
smartphones. Designing DNNs that can meet a stringent energy budget is becoming
increasingly important. This paper proposes ECC, a framework that compresses
DNNs to meet a given energy constraint while minimizing accuracy loss. The key
idea of ECC is to model the DNN energy consumption via a novel bilinear
regression function. The energy estimate model allows us to formulate DNN
compression as a constrained optimization that minimizes the DNN loss function
over the energy constraint. The optimization problem, however, has nontrivial
constraints. Therefore, existing deep learning solvers do not apply directly.
We propose an optimization algorithm that combines the essence of the
Alternating Direction Method of Multipliers (ADMM) framework with
gradient-based learning algorithms. The algorithm decomposes the original
constrained optimization into several subproblems that are solved iteratively
and efficiently. ECC is also portable across different hardware platforms
without requiring hardware knowledge. Experiments show that ECC achieves higher
accuracy under the same or lower energy budget compared to state-of-the-art
resource-constrained DNN compression techniques.
