In spatial computing, data-driven systems process vast data but face challenges due to complex algorithms and growing datasets, necessitating hardware scaling with higher costs. Compression is able to optimize memory and computational efficiency by removing unimportant information within the data. This is essential for edge computing, therefore, it is necessary to investigate the role of compression in spatial computing. Results show that compression improves systems efficiency at comparable small costs in accuracy, enabling edge computing in constrained environments.
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In spatial computing, data-driven systems process vast data but face challenges due to complex algorithms and growing datasets, necessitating hardware scaling with higher costs. Compression is able to optimize memory and computational efficiency by removing unimportant information within the data. This is essential for edge computing, therefore, it is necessary to investigate the role of compression in spatial computing. Results show that compression improves systems efficiency at comparable sma...
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