High-Density and High-Reliability Nonvolatile Field-Programmable Gate Array With Stacked 1D2R RRAM Array
Bibliographic record
Abstract
The huge area overhead of the interconnect is one of the critical issues in static random access memory (SRAM)-based field-programmable gate arrays (FPGAs), resulting in high power consumption and slow operation speed. Another critical issue is the volatile feature of the SRAM, which leads to high standby leakage current and long power-ON time. Resistive random access memory (RRAM) with a high resistance ratio and zero standby power possesses great potential in the FPGA applications. The conventional RRAM-based nonvolatile FPGAs (NVFPGAs) may use one-transistor 2-RRAM (1T2R) storage element to replace the SRAM or the one RRAM (1R) cell to replace both nMOS switch and SRAM. However, those NVFPGA schemes may suffer from the issues of low reliability, high configuration power, and high active leakage power. In this paper, we propose a novel element [one-diode two-RRAM (1D2R) cells] to replace the nMOS switch and 6 Transistors (6T) SRAM. Meanwhile, the novel block structures of the logic block, connection block, switch block, and the FPGA architecture based on the 1D2R element are proposed. Compared with the conventional 1T2R-based NVFPGA, our novel structure could improve the operation speed by 53% with a 40.5% lower operation power. Compared with the conventional 1R-based NVFPGA, the proposed scheme could greatly reduce the write error rate by eight orders with more than 20 times lower write power.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.001 | 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".