  Accelerating deep neural network (DNN) inference on resource-limited devices
is one of the most important barriers to ensuring a wider and more inclusive
adoption. To alleviate this, DNN binary quantization for faster convolution and
memory savings is one of the most promising strategies despite its serious drop
in accuracy. The present paper therefore proposes a novel binary quantization
function based on quantized compressed sensing (QCS). Theoretical arguments
conjecture that our proposal preserves the practical benefits of standard
methods, while reducing the quantization error and the resulting drop in
accuracy.
