  Recent trends show recognition accuracy increasing even more profoundly.
Inference process of Deep Convolutional Neural Networks (DCNN) has a large
number of parameters, requires a large amount of computation, and can be very
slow. The large number of parameters also require large amounts of memory. This
is resulting in increasingly long computation times and large model sizes. To
implement mobile and other low performance devices incorporating DCNN, model
sizes must be compressed and computation must be accelerated. To that end, this
paper proposes Binary-decomposed DCNN, which resolves these issues without the
need for retraining. Our method replaces real-valued inner-product computations
with binary inner-product computations in existing network models to accelerate
computation of inference and decrease model size without the need for
retraining. Binary computations can be done at high speed using logical
operators such as XOR and AND, together with bit counting. In tests using
AlexNet with the ImageNet classification task, speed increased by a factor of
1.79, models were compressed by approximately 80%, and increase in error rate
was limited to 1.20%. With VGG-16, speed increased by a factor of 2.07, model
sizes decreased by 81%, and error increased by only 2.16%.
