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High-Throughput Energy-Efficient Accelerator With Collaborative-Trainable Sparse-Quantization Method for On-Board Remote Sensing Processing
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DOI：10.1109/TGRS.2025.3616011.png)
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Abstract
En 中文
Convolutional neural networks (CNNs) have achieved remarkable breakthroughs on remote sensing tasks in recent years. However, deploying CNNs for real-time remote sensing on-board processing still remains a challenge due to power consumption, real-time, and other limitations. Therefore, in this article, a satellite-based real-time remote sensing accelerator is proposed, where algorithm and hardware approaches are proposed to jointly optimize CNNs' deployment on edge-side aerospace devices. First, a collaborative-trainable sparse-quantization (CTSQ) method is proposed to reduce the model's storage overhead. In the CTSQ method, analysis of the errors is performed for the sparsity-quantization composition. Besides, the interchannel correlations among parameters are leveraged, where the structured sparsity and quantization are performed with fine-grained units. Second, a modular-system co-optimized (MoSyC) architecture is proposed. A hardware-mapped sparse access (HMSA) strategy is proposed to effectively filter out zero elements in sparse parameters. Moreover, a high-throughput architecture is designed for parallel and pipelined data flow control. Finally, extensive experiments are conducted on both scene classification and object detection tasks with ResNet and YOLOv5 models. The results show that the proposed CTSQ method achieves the compression ratio of more than $13.81\times $ , and the proposed MoSyC architecture achieves the throughput of more than 1815 giga operations per second (GOPS), demonstrating the effectiveness of the proposed accelerator.
Keywords:
Quantization (signal)
Computer architecture
Neural networks
Training
Remote sensing
Hardware
Optimization
Real-time systems
Accuracy
Throughput
Convolutional neural network (CNN)
field programmable gate array (FPGA)
quantization
real-time
remote sensing
sparsity
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W