Retina: Cross-Layered Key-Value Store for Computational Storage
R. Madhava Krishnan, Naga Sanjana Bikonda, Shashwat Jain, Wook-Hee Kim, Hamid Hadian, Vishwanath Maram, Changwoo Min · 2023
We propose RETINA-a unified key-value store (KVS) with computational pipeline framework natively designed for computational storage. Retina proposes a cross-layered architecture to leverage CPU as the control plane and near-storage FPGA as the compute & data plane which is key to reducing the data movement and achieving high-performance. Retina Kvs includes near-storage Arbiter implemented on the FPGA which is capable of scheduling tasks, manage memory, and establish communication between the host CPU and the near-storage FPGA. Retina enables applications to compose and offload compute to the storage during the run time with a familiar set of KVS-style APIs. We evaluate Retina by integrating it to TensorFlow machine learning framework and training the ResNet50 DL model by offloading the entire image preprocessing steps to the near-storage FPGA. Overall, Retina performs up to 75% faster and saves up to 65% CPU time against CPU-only systems.