Design and Evaluation of an FPGA-based Hardware Accelerator for Deflate Data Decompression
Bibliographic record
Abstract
Data compression is an important technique for coping with the rapidly increasing volumes of data being transmitted over the Internet. The Deflate lossless data compression standard is used in several popular compressed file formats including the PNG image format and the ZIP and GZIP file formats. Consequently, several implementations of hardware accelerators for Deflate have been proposed. The recent availability of distributed field-programmable gate arrays (FPGAs) in the Internet cloud and the growing demand for decompressing compressed data that is streamed from remote servers make FPGA-based decompression accelerators commercially attractive. This paper describes an efficient implementation of the Deflate decompression algorithm using high-level synthesis from designs, specified in C++, down to optimized implementations for a Xilinx Virtex UltraScale+ class FPGA. When decompressing the Calgary corpus benchmark, our decompressor has average input (output) data throughputs of 70.7 (246.4) and 130.6 (386.6) MB/s for dynamically and statically encoded files, respectively. This performance is comparable to the 375 MB/s output throughput of Xilinx's state-of-the-art proprietary Deflate decompressor design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.004 | 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".