| 研究実績の概要 |
Recently, convolutional neural network (CNN) is actively used for image recognition while still has the bottleneck of energy efficiency due to the big amount of access data among the interfaces of imager, the memory and GPU. So far, several approaches including bit-precision-reduction of filter weights, networks-pruning and near/in-memory computation show the possibilities of low-energy consumption. In this work, we propose a near-pixel binary convolution engine to approach high energy-efficiency in convolutional layers by applying binary-input binary-weight CNN operations near pixels instead of long transmission signal lines.
|
