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 the designer faces the burden of packaging such buffers into on-chip memories and manually optimizing the utilization of each memory and the throughput of each buffer. In addition, the application memories may not match the word width or depth of the physical on-chip memories available on the FPGA. This process is time consuming and non-trivial, particularly with a large number of buffers of various depths and bit widths. We propose a tool, MPack, which globally optimizes on-chip memory use across all buffers for stream applications. The goal is to speed up development time by providing rapid design space exploration and relieving the designer of lengthy low-level iterations. We introduce new high-level pragmas allowing the user to specify global memory requirements, such as an application's on-chip memory budget and data throughput. We allow the user to quickly generate a large number of memory solutions and explore the trade-off between memory usage and achievable throughput. To demonstrate the effectiveness of our tool, we apply the new high-level pragmas to an image processing benchmark. MPack effectively explores the design space and is able to produce a large number of memory solutions ranging from 10 to 100% in throughput, and from 12 to 100% in on-chip memory usage.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.079 | 0.042 |
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".