End-to-end fully-binarized network design: from Generic Learned Thermometer to Block Pruning
Journal:
arXiv
Published Date:
May 5, 2025
Abstract
Existing works on Binary Neural Network (BNN) mainly focus on model's weights
and activations while discarding considerations on the input raw data. This
article introduces Generic Learned Thermometer (GLT), an encoding technique to
improve input data representation for BNN, relying on learning non linear
quantization thresholds. This technique consists in multiple data binarizations
which can advantageously replace a conventional Analog to Digital Conversion
(ADC) that uses natural binary coding. Additionally, we jointly propose a
compact topology with light-weight grouped convolutions being trained thanks to
block pruning and Knowledge Distillation (KD), aiming at reducing furthermore
the model size so as its computational complexity. We show that GLT brings
versatility to the BNN by intrinsically performing global tone mapping,
enabling significant accuracy gains in practice (demonstrated by simulations on
the STL-10 and VWW datasets). Moreover, when combining GLT with our proposed
block-pruning technique, we successfully achieve lightweight (under 1Mb),
fully-binarized models with limited accuracy degradation while being suitable
for in-sensor always-on inference use cases.
