InS-DLA: An In-SSD Deep Learning Accelerator for Near-Data Processing
Shengwen Liang, Ying Wang, Cheng Liu, Huawei Li, Xiaowei Li · 2019
Compute-centric architecture is suffering from the data moving overhead caused by memory wall, particularly for applications like large-scale data analysis based on deep learning technology. In this work, we designed an energy-efficient In-SSD Deep Learning Accelerator, InS-DLA, for Near-Data-Processing. InS-DLA directly operates on NAND Flash inside the Open Channel Solid-State-Drive where the target data are stored, eliminating the power and performance overhead caused by data movement. Experimental results reveal that the InS-DLA based FPGA prototype reduces energy consumption by 95.82% and 59.23%, compared to conventional CPU and GPU based deep learning systems.