VLSI Architecture for 8-Point AI-based Arai DCT having Low Area-Time Complexity and Power at Improved Accuracy
Bibliographic record
Abstract
A low complexity digital VLSI architecture for the computation of an algebraic integer (AI) based 8-point Arai DCT algorithm is proposed. AI encoding schemes for exact representation of the Arai DCT transform based on a particularly sparse 2-D AI representation is reviewed, leading to the proposed novel architecture based on a new final reconstruction step (FRS) having lower complexity and higher accuracy compared to the state-of-the-art. This FRS is based on an optimization derived from expansion factors that leads to small integer constant-coefficient multiplications, which are realized with common sub-expression elimination (CSE) and Booth encoding. The reference circuit [1] as well as the proposed architectures for two expansion factors α† = 4.5958 and α′ = 167.2309 are implemented. The proposed circuits show 150% and 300% improvements in the number of DCT coefficients having error ≤ 0:1% compared to [1]. The three designs were realized using both 40 nm CMOS Xilinx Virtex-6 FPGAs and synthesized using 65 nm CMOS general purpose standard cells from TSMC. Post synthesis timing analysis of 65 nm CMOS realizations at 900 mV for all three designs of the 8-point DCT core for 8-bit inputs show potential real-time operation at 2.083 GHz clock frequency leading to a combined throughput of 2.083 billion 8-point Arai DCTs per second. The expansion-factor designs show a 43% reduction in area (A) and 29% reduction in dynamic power (PD) for FPGA realizations. An 11% reduction in area is observed for the ASIC design for α† = 4.5958 for an 8% reduction in total power (PT ). Our second ASIC design having α′ = 167.2309 shows marginal improvements in area and power compared to our reference design but at significantly better accuracy.
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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 | 0.002 |
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".