Using buffer-to-BRAM mapping approaches to trade-off throughput vs. memory use
Bibliographic record
Abstract
One of the challenges in designing high-performance FPGA applications is fine-tuning the use of limited on-chip memory storage among many buffers in an application. To achieve desired performance and meet the on-chip memory budget requirements, the designer faces the burden of manually assigning application buffers to physical on-chip memories. Mismatches between dimensions (bit-width and depth) of buffers and physical on-chip memories lead to underutilized memories. Memory utilization can be increased via buffer packing - grouping buffers together and implementing them as a single memory, at the expense of data throughput. However, identifying buffer groups that result in the least amount of physical memory is a combinatorial problem with a large search space. This process is time consuming and non-trivial, particularly with a large number of buffers of various depths and bit widths. Previous work [1] introduced a tool that provides high-level pragmas allowing the user to specify global memory requirements, such as an application's on-chip memory budget and data throughput. This paper extends the previous work by introducing two low-level pragmas that specify information about memory access patterns, resulting in an improved on-chip memory utilization up to 22%. Further, we develop a simulated annealing based buffer packing algorithm, which reduces the tool's run-time from over 30 mins down to 15 sec, with an improvement in performance in the generated memory solution. Finally, we demonstrate the effectiveness of our tool with four stream application benchmarks.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".