Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and Layer Input Masking

Understand

Deep Neural Networks (DNNs) are increasingly deployed in highly energy-constrained environments such as autonomous drones and wearable devices while at the same time must operate in real-time.

• Therefore, reducing the energy consumption has become a major design consideration in DNN training.
• This paper proposes the first end-to-end DNN training framework that provides quantitative energy consumption guarantees via weighted sparse projection and input masking.
• The key idea is to formulate the DNN training as an optimization problem in which the energy budget imposes a previously unconsidered optimization constraint.