Resource and memory management techniques for the high-level synthesis of software threads into parallel FPGA hardware
Bibliographic record
Abstract
Recent work has proposed the high-level synthesis of parallel software programs (specified using Pthreads or OpenMP) into concurrently operating parallel hardware modules [6]. In this paper, we describe resource and memory management techniques for improving performance and area of hardware generated by such software thread synthesis. One direction investigated pertains to how modules in the HLS-generated parallel hardware should connect to one another: 1) with a nested topology, or 2) with a flat topology. In the nested topology, hardware modules are created in a hierarchical manner: modules are instantiated inside within modules that use them. Conversely, the flat topology instantiates all hardware modules at the same level of hierarchy. For the flat topology, we describe a system generator that automatically generates the required interconnect between all hardware modules, as well as flexibly shares or replicates functions, functional units, and memories. We also explore methods to reduce memory contention among hardware units that operate in parallel, by investigating three different memory architectures which use: 1) a global memory controller, 2) local memories, and 3) shared-local memories. Local and shared-local memories are dedicated RAM blocks for a single or a set of hardware modules, and help to increase memory bandwidth by allowing concurrent memory accesses. We also consider memory replication to localize memories in hardware modules, and convert small memories to registers to further improve performance and memory usage. Finally, we describe implementing locks and barriers in HLS hardware: synchronization constructs used in parallel programming. We show that with our resource and memory management techniques, we can improve the geomean performance, area, and area-delay product of parallel HLS-generated hardware up to 41.6%, 38.3%, and 63.3%, respectively, for a set of 15 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 | 0.001 |
| 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 | 0.001 |
| 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".