Evaluation of High Level Synthesis Frameworks: Analysis Across Three Programming Paradigms
Bibliographic record
Abstract
High Level Synthesis (HLS) has played an important role in the design of high performance Field Programmable Gate Array (FPGA) based solutions and it is increasingly popular among developers.In this thesis, two HLS tools, Vivado HLS and Clash, supporting three programming language paradigms (imperative, transactionlevel modeling, and functional) are investigated.To assess the characteristics and performance of these HLS tools, we implement two trivial and non-trivial applications, Finite Impulse Response (FIR) filter and EigenValue Decomposition (EVD) with C++, SystemC and Clash.Pre-synthesis and post-synthesis results are elaborated to ensure each tool generates consistent outputs and simulation results are verified with the same program in MATLAB.It was found that Clash usually has better performance and latency as a result of the intrinsic parallelism feature of functional programming language Haskell.However, it was also found Vivado HLS has better results regarding power consumption and resource utilization, and provides more options for optimization of a design.i
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".