| Abstract: | Deep learning algorithms are the basis of many artificial intelligence applications. Those algorithms are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems. Thus various deep learning accelerators(DLAs) are proposed and applied to achieve better performance and lower power consumption. However, most deep learning accelerators are unable to support multiple data formats. This research proposes the MW-DLA, a deep learning accelerator supporting dynamic configurable data-width. This work analyzes the data distribution of different data types in different layers and trains a typical network with per-layer representation. As a result, the proposed MW-DLA achieves 2 X performance and more than 50% memory requirement for AlexNet with less than 5.77% area overhead. |
