Design and Architectural Assessment of 3-D Resistive Memory Technologies in FPGAs
Bibliographic record
Abstract
Emerging nonvolatile memories (eNVMs) such as phase-change random access memories (PCRAMs) or oxide-based resistive random access memories (OxRRAMs) are promising candidates to replace Flash and Static Random Access Memories in many applications. This paper introduces a novel set of building blocks for field-programmable gate arrays (FPGAs) using eNVMs. We propose an eNVM-based configuration point, a look-up table structure with reduced programming complexity and a high-performance switchbox arrangement. We show that these blocks yield an improvement in area and write time of up to 3× and 33×, respectively, versus a regular Flash implementation. By integrating the designed blocks in an FPGA, we demonstrate an area and delay reduction of up to 28% and 34%, respectively, on a set of benchmark circuits. These reductions are due to the eNVM 3-D integration and to their low on-resistance state value. Finally, we survey many flavors of the technologies and we show that the best results in terms of area and delay are obtained with Pt/TiO2/Pt stack, while the lowest leakage power is achieved by InGeTe stack.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".