ISVDA: An in Storage Processing Accelerator for Visual Data Analysis

Deep neural networks(DNNs) have been widely applied in visual data analysis. To accelerate DNN-based visual data inference, contemporary visual data analytics systems employ strategies like model specialization and model cascading to alleviate the computational demands of DNN processes. In addition, some existing systems seek to reduce the overhead of data decoding by directly storing decoded RGB data on storage devices, albeit at the expense of significantly increased storage costs and data transfer volumes. In this paper, we introduce ISVDA, a visual data analysis accelerator based on In-storage computing. ISVDA proposes to offload the data-intensive video preprocessing tasks, encompassing data decoding and resizing, to the computational storage device, leveraging its ample internal bandwidth and computational advantages. Concurrently, ISVDA introduces a dynamic sampling algorithm that intelligently adjusts sampling strides based on spatiotemporal information encoded in motion vectors, thereby skipping more video frames for DNN inferences in both retrospective and streaming analysis scenarios. Experiments conducted on real hardware demonstrate that compared to CPU-based preprocessing systems, ISVDA can achieve average 6.8x speedup for image data analysis and 2.8x speedup for video data analysis.