Bio-inspired solutions are used to solve the real-world problems as they are able to resolve the complex issues. Already existing bio-inspired solutions are reviewed in this chapter which solved the complex engineering problems. Moreover, this chapter also discusses the implementation of biological brain mechanism in Network on Chip to address the fault-tolerant issues. Network on Chip (NoC) is a communication framework for System on Chip (SoC). Due to routers and interconnect failure, NoC suffers from faults. Therefore, bio-inspired solutions help us to recover from these faults. The techniques from the biological brain were implemented in NoC as the brain is fault tolerant and highly adaptive. Results showed that bio-inspired techniques are performing well compared to the traditional fault-tolerant algorithms.
Scientists are always attracted by the bio-inspired techniques to solve the difficult engineering world problems. These techniques are being used as the novel way to solve the faulty situation in Network on Chip (NoC). Faults in NoC arises due to big sizes of interconnects as the size of the devices were continuously reduced to cope with the communication requirement of processing elements (PE's). Due to these faults a lot of conventional fault tolerant techniques have been proposed. But all of these techniques have drawbacks of latency, less bandwidth utilization and lesser throughput. In this paper, a novel bio-inspired technique "sprouting" is proposed. Bioinspired sprouting algorithm is based on biological brain technique which makes the algorithm robust and the NoC fault tolerant. The result shows that the bio-inspired algorithm efficiently utilizes the bandwidth and throughput, packet network latency is degrading gracefully during the network recovery from fault. The average packet network latency increases 20.51%, NoC bandwidth reduces 0.471% and throughput is drop to 37.22% during the recovery from faults.
Whenever it comes to data processing, the user always faces two major constraints. One is storage capacity and second is bandwidth. These two resources must be efficiently utilized by compressing the data. Enormous algorithms are used to compress data. As far as, compression in storage is concern, GZIP is used on large scale for lossless data compression. However, it is not desirable to carry out lossless data compression for real time data. In this paper, an improvisation is proposed in the existing GZIP algorithm for compressing real time data by a contemporary concept of introducing Adaptive Huffman algorithm by replacing the traditional Huffman encoder (static). Simulations have proved that improvised GZIP has approximate 18% better compression ratio and space saving than traditional GZIP for real time data. This research paper extends the usability of GZIP algorithm to carry out lossless compression for real time data.
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