Leveraging Seed Generation for Efficient Hardware Acceleration of Lossless Compression of Remotely Sensed Hyperspectral Images
In the field of satellite imaging, effectively managing the enormous volumes of data from remotely sensed hyperspectral images presents significant challenges due to the limited bandwidth and power available in spaceborne systems. In this paper, we describe the hardware acceleration of a highly efficient lossless compression algorithm, specifically designed for real-time hyperspectral image processing on FPGA platforms. The algorithm utilizes an innovative seed generation method for square root calculations to significantly boost data throughput and reduce energy consumption, both of which represent key factors in satellite operations. When implemented on the Cyclone V FPGA, our method achieves a notable operational throughput of 1598.67 Mega Samples per second (MSps) and maintains a power requirement of under 1 Watt, leading to an efficiency rate of 1829.1 MSps/Watt. A comparative analysis with existing and related state-of-the-art implementations confirms that our system surpasses conventional performance standards, thus facilitating the efficient processing of large-scale hyperspectral datasets, especially in environments where throughput and low energy consumption are prioritized.
EDITED BOOK: B. Ben Youssef and M. M. Ben Ismail (Editors.). Integrating Machine Learning into HPC-Based Simulations and Analytics. IGI Global Scientific Publishing, Hershey,…
In classical machine learning algorithms, used in many analysis tasks, the data are cen-
tralized for training. That is, both the model and the data are housed within one device. Federated…
In the field of satellite imaging, effectively managing the enormous volumes of data from remotely sensed hyperspectral images presents significant challenges due to the limited bandwidth and…