Comparative Analysis of On-Chip FPGA Memory Architectures for Viterbi Decoder Implementation in DVB Systems
Bibliographic record
Abstract
High-definition Digital Video Broadcasting (DVB) systems demand high data rates, resulting in increased hardware complexity and power consumption, with the Viterbi decoder (VD) being a key contributor. The substantial memory resources required for these high data rates drive this research, which investigates the impact of diverse static random-access memory (SRAM) architectures on Zynq FPGA and their embedded memory resources, aiming to design power-efficient and less complex Viterbi decoders. Besides, the effect of different memories architectures on the transceiver (Tx-Rx) system has been studied. Viterbi decoder is implemented with different memory architectures on xczu7ev-2ffvc1156: flip flops, distributed RAM, block ram (BRAM), and UltraRAMTM (URAM). BRAM IP from AMD has significantly improved the dynamic power of Viterbi decoder by 97% compared to other available memories. Effectiveness of the employed BRAM is ensured by saving about 50% of the total power of the baseband transceiver system. That Tx-Rx operates at a frequency of 125MHz with a throughput of 62.3 Mbps, a code rate: ½, and 16APSK modulation scheme. Viterbi decoder has achieved a reduction in power compared to sleepy keeper and space time trellis code (STTC) with about 44% and 61% respectively. Functionality of the proposed VD architecture for signal to noise ratio (SNR) of 0 dB at additive white Gaussian noise (AWGN) channel and vector length of 3,264 bits is verified. Hardware validation on ZCU104 based on DVB standard is also done and reported.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".