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Syst."],"published-print":{"date-parts":[[2026,1,31]]},"abstract":"<jats:p>\n                    In this work, we present our intelligent SSD,\n                    <jats:italic toggle=\"yes\">SeeSSD<\/jats:italic>\n                    , an energy-efficient computational SSD for a real-time object detection system. SeeSSD embeds an FPGA-based CNN processing engine and the firmware that performs the convolutional operation on the target image. SeeSSD processes the image data at the storage before sending it to the host. This reduces the amount of data transferred to the host and lowers the data movement overhead, thus reducing transfer time and saving power. By using our SeeSSD system and YOLO_Embed, an object detection neural network model, we are able to outperform the fastest YOLO model for an embedded controller, YOLO-Lite, in terms of performance, accuracy, and energy efficiency. YOLO (You Only Look Once) models are a series of one-stage object detection neural models that have become very popular due to their fast speed and high accuracy. The contribution of this work includes designing and implementing our SeeSSD system with a lightweight object detection model, YOLO_Embed, for reducing the data movement overhead, performing real-time inference, and lowering the overall power consumption. We implemented the entire software stack associated with the SeeSSD system; on-device CNN acceleration engine implemented on FPGA, object identification interface for SeeSSD using YOLO_Embed, and embedded software layer in SeeSSD for on-device convolutional processing. We calculated our YOLO_Embed model\u2019s accuracy on object detection dataset benchmarks such as PASCAL VOC 2012, which came out to be 38.1% mAP (mean Accuracy Precision). Our system was able to perform inference in 0.21 seconds while reducing the power consumption by approximately 1.2\u00d7 and 1.4\u00d7 for CPU-Only and CPU+GPU systems, respectively. We were also able to reduce the data movement overhead by 24\u00d7 for a single target image.\n                  <\/jats:p>","DOI":"10.1145\/3774649","type":"journal-article","created":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T11:41:01Z","timestamp":1762861261000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["SeeSSD: Computational Storage for Energy-Efficient Real-Time Object Detection"],"prefix":"10.1145","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1969-5968","authenticated-orcid":false,"given":"Muhammad Danish","family":"Tehseen","sequence":"first","affiliation":[{"name":"Electrical Engineering, KAIST","place":["Daejeon, Korea (the Republic of)"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4668-7680","authenticated-orcid":false,"given":"Gyeongcheol","family":"Shin","sequence":"additional","affiliation":[{"name":"Electrical Engineering, KAIST","place":["Daejeon, Korea (the Republic of)"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1099-1496","authenticated-orcid":false,"given":"Joo-Young","family":"Kim","sequence":"additional","affiliation":[{"name":"Electrical Engineering, KAIST","place":["Daejeon, Korea (the Republic of)"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7178-5245","authenticated-orcid":false,"given":"Youjip","family":"Won","sequence":"additional","affiliation":[{"name":"KAIST","place":["Daejeon, Korea (the Republic of)"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,1,6]]},"reference":[{"key":"e_1_3_1_2_2","volume-title":"Proceedings of the 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS)","author":"Adarsh Pranav","year":"2020","unstructured":"Pranav Adarsh, Pratibha Rathi, and Manoj Kumar. 2020. 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