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Deep neural networks have significantly improved performance on a range oftasks with the increasing demand for computational resources, leavingdeployment on low-resource devices (with limited memory and battery power)infeasible. Binary neural networks (BNNs) tackle the issue to an extent withextreme compression and speed-up gains compared to real-valued models. Wepropose a simple but effective method to accelerate inference through unifyingBNNs with an early-exiting strategy. Our approach allows simple instances toexit early based on a decision threshold and utilizes output layers added todifferent intermediate layers to avoid executing the entire binary model. Weextensively evaluate our method on three audio classification tasks and acrossfour BNNs architectures. Our method demonstrates favorable quality-efficiencytrade-offs while being controllable with an entropy-based threshold specifiedby the system user. It also results in better speed-ups (latency less than 6ms)with a single model based on existing BNN architectures without retraining fordifferent efficiency levels. It also provides a straightforward way to estimatesample difficulty and better understanding of uncertainty around certainclasses within the dataset.

Binary Early-Exit Network for Adaptive Inference on Low-Resource Devices