High-Level Description and Synthesis of Floating-Point Accumulators on FPGA
Bibliographic record
Abstract
Decades of research in the field of high level hardware description now result in tools that are able to automatically transform C/C++ constructs into highly optimized parallel and pipelined architectures. Such approaches work fine when the control flow is a priory known since the computation results in a large dataflow graph that can be mapped into the available operators. Nevertheless, some applications have a control flow that is highly dependant on the data. This paper focuses on the hardware implementation of such applications and presents a high level synthesis methodology applied to a Hardware Description Language (HDL) in which assignments correspond to self-synchronized connections between predefined data streaming sources and sinks. A data transfer occurs over an established connection when both source and sink are ready, according to their synchronization interfaces. Founded on a high-level communicating FSM programming model, the language allows the user to describe and dynamically modify streaming architectures exploiting spatial and temporal parallelism. Our compiler attempts to maximize the number of transfers at each clock cycle and automatically fixes the potential combinatorial loops induced by the dynamic connection of dependant sources and sinks. The methodology is applied to the synthesis of a pipelined floating point accumulator using the Delayed-Buffering (DB) reduction method. The results we obtain are similar to state-of-the-art dedicated architectures but require much less design time and expertise.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".