  Application-specific integrated circuit (ASIC) implementations for Deep
Neural Networks (DNNs) have been adopted in many systems because of their
higher classification speed. However, although they may be characterized by
better accuracy, larger DNNs require significant energy and area, thereby
limiting their wide adoption. The energy consumption of DNNs is driven by both
memory accesses and computation. Binarized Neural Networks (BNNs), as a
trade-off between accuracy and energy consumption, can achieve great energy
reduction, and have good accuracy for large DNNs due to its regularization
effect. However, BNNs show poor accuracy when a smaller DNN configuration is
adopted. In this paper, we propose a new DNN model, LightNN, which replaces the
multiplications to one shift or a constrained number of shifts and adds. For a
fixed DNN configuration, LightNNs have better accuracy at a slight energy
increase than BNNs, yet are more energy efficient with only slightly less
accuracy than conventional DNNs. Therefore, LightNNs provide more options for
hardware designers to make trade-offs between accuracy and energy. Moreover,
for large DNN configurations, LightNNs have a regularization effect, making
them better in accuracy than conventional DNNs. These conclusions are verified
by experiment using the MNIST and CIFAR-10 datasets for different DNN
configurations.
