USENIX ATC '19: 2019 USENIX Annual Technical Conference

Data analysis and retrieval is a widely-used component in existing artificial intelligence systems. However, each request has to go through each layer across the I/O stack, which moves tremendous irrelevant data between secondary storage, DRAM, and the on-chip cache. This leads to high response latency and rising energy consumption. To address this issue, we propose Cognitive SSD, an energy-efficient engine for deep learning based unstructured data retrieval. In Cognitive SSD, a flash-accessing accelerator named DLG-x is placed by the side of flash memory to achieve near-data deep learning and graph search. Such functions of in-SSD deep learning and graph search are exposed to the users as library APIs via NVMe command extension. Experimental results on the FPGA-based prototype reveal that the proposed Cognitive SSD reduces latency by 69.9% on average in comparison with CPU based solutions on conventional SSDs, and it reduces the overall system power consumption by up to 34.4% and 63.0% respectively when compared to CPU and GPU based solutions that deliver comparable performance.