E4SA: An Ultra-Efficient Systolic Array Architecture for 4-Bit Convolutional Neural Networks
Bibliographic record
Abstract
Many studies have demonstrated that 4-bit precision quantization can achieve comparable accuracy to floating-point DNNs, sparking significant interest in efficiently accelerating compressed DNNs, especially 4-bit convolutions, on edge devices. However, we observe that conventional systolic array (SA) architectures designed for DNNs cannot fully exploit the advantages of high DSP computational density offered by 4-bit DSP packing. Although state-of-the-art FPGA-based SA architectures (e.g., AutoSA) exhibit flexibility in accommodating 4-bit DSP packing, they suffer from resource consumption and data supply latency issues, especially when adapting to various convolution spatial sizes. This work introduces a customizable and ultra-efficient SA architectural template for 4-bit convolution, called E4SA. First, we propose a fine-grained row-temporal weight stationary dataflow that aligns with the specific requirements of 4-bit DSP full packing (4bF packing). Based on this, we design a cost-effective SA unit (SAU) composed of 4bF-packing-based processing elements (PEs) to enhance computational efficiency. This includes column-shared packed-data splitters and shift-register-based feature-map/weight fetchers to ensure continuous data supply, all of which are locally interconnected via more cost-effective registers. In addition, we develop a two-level hierarchy SA that decomposes the original large SA into parallel 4×4 SAU sets, which not only allows multiple PEs in the same column to share data splitting and reorganization logic and thus reducing the LUT overhead, but also maintains near-theoretical latency across various convolutional spatial sizes. Experimental results demonstrate that E4SA achieves up to 576.6 GOPS with 13.8× higher GOPS/DSP efficiency and 51.6× higher GOPS/kLUTs efficiency compared to 4-bit AutoSA-based design.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".