High-Throughput FPGA-Based Hardware Accelerators for Deflate Compression and Decompression Using High-Level Synthesis
Bibliographic record
Abstract
The Deflate compression algorithm provides one of the most widely used solutions for lossless data compression. Field-programmable gate arrays (FPGAs) are commonly used to implement hardware accelerators that speed up computation-intensive applications. In this article, FPGA-based accelerators for Deflate compression and decompression are described. These accelerators were specified in C++ and synthesized using Vivado High-Level Synthesis (HLS) for a Xilinx Virtex UltraScale+ series FPGA and a system clock frequency of 250 MHz. The proposed compressor processes data at a fixed input throughput of 4.0 GB/s and achieves a geometric mean compression ratio of 1.92 on the Calgary corpus benchmark files using static Huffman encoding. While not the first compressor synthesized using high-level synthesis, our design achieves a 25% greater throughput and an 11% greater compression ratio than the only other published design that uses Vivado HLS. The proposed decompressor design achieves average input throughputs of 196.61 MB/s and 97.40 MB/s, for statically and dynamically encoded Calgary corpus files, respectively. This is the first published decompressor design that is synthesized using high-level synthesis and provides performance that is comparable to that of the best published designs, having static throughputs 11% higher and dynamic throughputs only 10% lower than the expertly-optimized design sold by Xilinx.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".