Nov 7, 2019 · The accuracy improvement of neural networks may be only a few percentage points while the computation effort explodes, as previous less ...

ABSTRACT. The accuracy improvement of neural networks may be only a few percentage points while the computation effort explodes, as.

We employ a lightweight prediction based Big/Little design to process those "easy" inputs with a little DNN and those "difficult" inputs with a big DNN.

A novel concept called big/LITTLE DNN (BL-DNN) which significantly reduces energy consumption required for DNN execution at a negligible loss of inference ...

Lightweight prediction based big/little design for efficient neural network inference. Y Tian, M Li, Q Xu. Proceedings of the 4th ACM/IEEE Symposium on Edge ...

Lightweight prediction based big/little design for efficient neural network inference. Y Tian, M Li, Q Xu. Proceedings of the 4th ACM/IEEE Symposium on Edge ...

Lightweight prediction based big/little design for efficient neural network inference. SEC 2019: 356; 2018. [c7]. view. electronic edition via DOI · unpaywalled ...

One of the first yet most effective approaches is the so-called "big/little" system, in which two classifiers of different complexity and accuracy are combined ...

Apr 19, 2023 · For example, Google has delivered a light version of the Tensor Processing Unit (TPU) called Edge TPU which is able to provide power-efficient.

This paper investigates the energy savings that near-subthreshold processors can obtain in edge AI applications and proposes strategies to improve them ...