E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savings

Understand

Convolutional neural networks (CNNs) have been increasingly deployed to edge devices.

• Hence, many efforts have been made towards efficient CNN inference in resource-constrained platforms.
• This paper attempts to explore an orthogonal direction: how to conduct more energy-efficient training of CNNs, so as to enable on-device training.
• We strive to reduce the energy cost during training, by dropping unnecessary computations from three complementary levels: stochastic mini-batch dropping on the data level; selective layer update on the model level; and sign prediction for low-cost, low-precision back-propagation, on the algorithm level.