  Deep neural networks (DNN) are powerful models for many pattern recognition
tasks, yet their high computational complexity and memory requirement limit
them to applications on high-performance computing platforms. In this paper, we
propose a new method to evaluate DNNs trained with 32bit floating point
(float32) accuracy using only low precision integer arithmetics in combination
with binary shift and clipping operations. Because hardware implementation of
these operations is much simpler than high precision floating point
calculation, our method can be used for an efficient DNN inference on dedicated
hardware. In experiments on MNIST, we demonstrate that DNNs trained with
float32 can be evaluated using a combination of 2bit integer arithmetics and a
few float32 calculations in each layer or only 3bit integer arithmetics in
combination with binary shift and clipping without significant performance
degradation.
