Racetrack Memory-Based Nonvolatile Storage Elements for Multicontext FPGAs
Bibliographic record
Abstract
A multicontext field-programmable gate array (FPGA) is a solution to achieve fast run-time reconfiguration. However, SRAM-based multicontext FPGAs still suffer from high leakage power during sleep, slow power-ON speed, and excessive large memory area. Racetrack memory is one of the most promising resistive nonvolatile memories, with the advantages of low power, high density, and high speed. In this paper, we propose two racetrack memory-based nonvolatile storage elements (NVSEs) for multicontext FPGAs. One is the shifting-based NVSE (type-1) with the advantages of high density and low power. The other one is the address-based NVSE (type-2) with the advantages of high context switching speed and low context switching power. The versatile place and route simulation results show that the type-1 NVSE-based eight-context FPGA reduces the area, critical path delay, and the power of the SRAM-based eight-context FPGA by more than 68.1%, 22.8%, and 13%, respectively. The proposed type-2 NVSE-based FPGAs allow the contexts to be switched 4.46 times faster than the type-1 NVSE-based FPGAs. Both designs improve the FPGA power-ON speed by more than a million times. Compared with the conventional racetrack memory-based lookup table (LUT), the proposed racetrack memory-based LUT may reduce the total power by more than 25%.
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) | 0.003 | 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".