PADCIM: A 1966.9-TOPS/W 2.09%-RMSE Probabilistic Approximate Dynamic-Logic Based Digital Computing-in-Memory Macro in 40nm
Bibliographic record
Abstract
This work presents a 1966.9-TOPS/W 2.09%-RMSE digital computing-in-memory (CIM) macro named PADCIM. It has three key features: 1) Probabilistic approximate adder tree (PAAT) coupled with delay clock chain, achieve high energy efficiency and low RMSE while resolving slope deviation. 2) Full dynamic-logic computing circuits (FDCC) combined with NP-Domino cascading, reduce chip power and area. 3) XOR-based multi-bit computation scheme (XMCS) integrated with bias-error compensation, enable accurate binary and multi-bit computations. Fabricated in 40 nm technology, PADCIM demonstrates$1.12 \times$higher energy efficiency and$1.92 \times$lower RMSE than the SOTA ADCIM. It further achieves up to$\mathbf{1. 7 0} {\times}$higher energy efficiency over$28 \text{nm} / 12 \text{nm}$-nodes chips, while delivering 87.56 % (1b/1b) and 91.19% (4b/1b) inference accuracy on CIFAR-10.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".