High density, low energy, magnetic tunnel junction based block RAMs for memory-rich FPGAs
Bibliographic record
Abstract
Many important applications demand large amounts of on-chip memory both to fully utilize an FPGA's computational capacity and to minimize energy-consuming off-chip memory accesses, leading some recent commercial FPGAs to add higher-capacity on-chip block RAMs (BRAMs). While memory is becoming more important to FPGA designs, SRAM scaling is becoming more difficult because of increasing device variation. An alternative is to build FPGA BRAM from magnetic tunnel junction (MTJ) cells as this emerging embedded memory features a small cell size, low energy usage, and good scalability. In this work, we conduct a detailed comparison study of SRAM and MTJ BRAMs that includes cell designs that are robust with device variation, transistor-level design and optimization of all the required BRAM-specific circuits, and variation-aware simulation at the 22nm node. We find that as the capacity of a BRAM increases, the MTJ benefits of high-density and low-energy increase and its drawback of lower speed is mitigated. At a 256 Kb block size, MTJ-BRAM is 3.06× denser and 55% more energy efficient and its Fmaxis 274 MHz, which is adequate for most FPGA system clock domains. We detail how the non-volatility of an MTJ-BRAM saves energy, especially for narrow write operations which are common for the width-configurable BRAMs of FPGAs. For a RAM architecture similar to the latest commercial FPGAs, MTJ-based block RAMs reduce the FPGA fabric area by 28%, or alternatively could expand FPGA memory capacity by 2.95× with no die size increase.
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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".