SeeSSD: Computational Storage for Energy-Efficient Real-Time Object Detection
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Bibliographic record
Abstract
In this work, we present our intelligent SSD, SeeSSD , 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’s 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× and 1.4× for CPU-Only and CPU+GPU systems, respectively. We were also able to reduce the data movement overhead by 24× for a single target image.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it