Moreover, the algorithm is decentralized, requires no communication between the agents, has minimal memory requirements, and can reflect variations in the computational speeds of the agents.
The developed S-DDM scheme reduces computational complexities, large memory requirements and computational costs.
It is concluded that the method leads to considerable economies both in computer memory requirements and in CPU time.
It reduces computational complexities, large memory requirements, and long computation durations due to the application of the splitting technique.
The whole process is automatic, is fast, has moderate CPU and memory requirements, and compares favorably to other existing techniques.
Low memory requirements and modest computational complexity facilitates calculations on high-resolution three-dimensional models.
Also, the proposed architectures outperform the existing one in terms of the memory requirements and area.
Memory requirements and critical path are essential for 2-D Discrete Wavelet Transform (DWT).
As a decoding method, the Fano algorithm is selected for its relatively low memory requirements and computational complexity.
However, the hardware architecture has significant impact on the decoding throughput, logic and memory requirements, and power consumption.
Various studies have shown that code compression can significantly reduce memory requirements, and may improve performance in many scenarios.
