Hardware optimization for effective switching power reduction during data compression in GOLOMB rice coding

• Published in: PloS one Vol. 19; no. 9; p. e0308796
• Main Authors: Sakthivel, R., Vijayalakshmi, Ch, Vanitha, M., AboRas, Kareem M., Abdelfattah, Waleed Mohammed, Ghadi, Yazeed Yasin, Rami Reddy, Ch
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 26.09.2024
• Subjects: Algorithms; Analysis; Biology and Life Sciences; Codes; Coding; Compression; Compression ratio; Computation; Computer and Information Sciences; Computer Simulation; Computers; Data compression; Data Compression - methods; Data mining; Dictionaries; Electrocardiography; Embedded systems; Engineering and Technology; Evaluation; Hardware; Lossless equipment; Oryza; Physical Sciences; Power consumption; Research and Analysis Methods; Rice; Systems design; Very-large-scale integration; India
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0308796

Loss-less data compression becomes the need of the hour for effective data compression and computation in VLSI test vector generation and testing in addition to hardware AI/ML computations. Golomb code is one of the effective technique for lossless data compression and it becomes valid only when the divisor can be expressed as power of two. This work aims to increase compression ratio by further encoding the unary part of the Golomb Rice (GR) code so as to decrease the amount of bits used, it mainly focuses on optimizing the hardware for encoding side. The algorithm was developed and coded in Verilog and simulated using Modelsim. This code was then synthesised in Cadence Encounter RTL Synthesiser. The modifications carried out show around 6% to 19% reduction in bits used for a linearly distributed data set. Worst-case delays have been reduced by 3% to 8%. Area reduction varies from 22% to 36% for different methods. Simulation for Power consumption shows nearly 7% reduction in switching power. This ideally suggest the usage of Golomb Rice coding technique for test vector compression and data computation for multiple data types, which should ideally have a geometrical distribution.