  Deep neural networks are state-of-the-art models for understanding the
content of images, video and raw input data. However, implementing a deep
neural network in embedded systems is a challenging task, because a typical
deep neural network, such as a Deep Belief Network using 128x128 images as
input, could exhaust Giga bytes of memory and result in bandwidth and computing
bottleneck. To address this challenge, this paper presents a hardware-oriented
deep learning algorithm, named as the Deep Adaptive Network, which attempts to
exploit the sparsity in the neural connections. The proposed method adaptively
reduces the weights associated with negligible features to zero, leading to
sparse feedforward network architecture. Furthermore, since the small
proportion of important weights are significantly larger than zero, they can be
robustly thresholded and represented using single-bit integers (-1 and +1),
leading to implementations of deep neural networks with sparse and binary
connections. Our experiments showed that, for the application of recognizing
MNIST handwritten digits, the features extracted by a two-layer Deep Adaptive
Network with about 25% reserved important connections achieved 97.2%
classification accuracy, which was almost the same with the standard Deep
Belief Network (97.3%). Furthermore, for efficient hardware implementations,
the sparse-and-binary-weighted deep neural network could save about 99.3%
memory and 99.9% computation units without significant loss of classification
accuracy for pattern recognition applications.
