TableCache: An Open-Source, Configurable, Last-Level Cache for FPGA Systems
Bibliographic record
Abstract
The performance of FPGA systems is increasingly limited by the latency and bandwidth of off-chip memory. The traditional ASIC solution of using caches has also been both studied and implemented on FPGAs. However, such works have largely focused on primary caches, and fail to optimize for the unique resources modern FPGAs offer like LUTRAMs. Additionally, many recent developments in ASIC caching (e.g. new cache replacement policies and victim caches) have not been evaluated on FPGA systems, which provide less opportunity for improvement because of lower cache miss latencies. This work presents TableCache: an open-source, configurable, last-level cache for FPGA systems that makes prolific use of LUTRAM-based structures. This allows our design to use 51% fewer LUTs than a directly-comparable commercial FPGA lastlevel cache while offering lower read-hit latency and running on average at 29% higher frequencies. The parameterization of TableCache also allows us to explore different cache replacement policies, where we find that Static Re-Reference Interval Prediction (SRRIP) can reduce average memory access time by 6.4% compared to Least Recently Used (LRU) while using fewer FPGA resources. We also find that the impact of last-level victim caching on FPGAs is largely negative, with a marginal 0.21% reduction in average memory access time (measured in cycles) significantly outweighed by a 32.4% increase to cache LUTs and a 16.9% reduction in operating frequency.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".