Dual-Thread Deflate/Inflate Accelerator With Multicheckpoint Control With High Throughput and Compression Ratio for Bandwidth-Efficient Systems
Bibliographic record
Abstract
With the exponential growth of data volumes in AI training and prediction systems, the cost and resource demands of data transmission have emerged as critical challenges. Lossless data compression effectively reduces data size, transmission bandwidth, and latency while preserving data integrity. This article presents a fully pipelined lossless CODEC integrating Deflate compression and Inflate decompression accelerators. The proposed Deflate implementation employs match filtering and pair merging strategies to enhance compression ratios. We introduce three key innovations for the Inflate decompressor: 1) a dual-thread architecture with multicheckpoint control; 2) optimized end-of-block (EOB) handling in Huffman coding; and 3) a rewinding mechanism in LZ77 decoding. FPGA implementation results demonstrate that our Deflate compressor achieves 16 bytes/cycle throughput with an average compression ratio of 2.26, surpassing state-of-the-art implementations. The 28 nm CMOS implementation shows Inflate decompression throughputs of 1431.85 MB/s (dynamic Huffman) and 1324.26 MB/s (static Huffman) on the Calgary Corpus dataset. Notably, our 28 nm CMOS-based decompressor achieves$1.16\times $higher throughput than recent 14 nm implementations in spite of operating at half their maximum frequency.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".