High-Throughput FPGA-Based Hardware Accelerators for Deflate Compression and Decompression Using High-Level Synthesis
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it