Bitwidth-optimized hardware accelerators with software fallback
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We propose the high-level synthesis of an FPGA-based hybrid computing system, where the implementations of compute-intensive functions are available in both software, and as hardware accelerators. The accelerators are optimized to handle common-case inputs, as opposed to worst-case inputs, allowing accelerator area to be reduced by 28%, on average, while retaining the majority of performance advantages associated with a hardware versus software implementation. When inputs exceed the range that the hardware accelerators can handle, a software fallback is automatically triggered. Optimization of the accelerator area is achieved by reducing datapath widths based on application profiling of variable ranges in software (under typical datasets). The selected widths are passed to a high-level synthesis tool which generates the accelerator for a given function. The optimized accelerators with software fallback capability are generated automatically by our framework, with minimal user intervention. Our study explores the trade-offs of delay and area for benchmarks implemented on an Altera Cyclone II FPGA.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it