Efficient FPGA based architecture for high‐order FIR filtering using simultaneous DSP and LUT reduced utilization
Bibliographic record
Abstract
Abstract This paper proposes an efficient high‐order finite impulse response (FIR) filter structure for field programmable gate array (FPGA)‐based applications with simultaneous digital signal processing (DSP) and look‐up‐table (LUT) reduced utilization. The real‐time updating of the filter coefficients is also put into perspective. In order to perform these objectives, both the speed and the structure of FPGA are efficiently exploited. The gap between the required input sampling frequency and the FPGA allowed maximum frequency is managed to achieve additional computing sequences. Furthermore, the special structures of the FPGA Look‐up‐table Shift‐Register (LUT‐SR) and their internal connections are fully employed for pipelining and selecting the input samples. The FPGA Block RAMs (BRAMs) are employed for handling the reconfigurable filter coefficients, and the FPGA DSP slices are associated for computing the output data of the BRAMs and the multiplexers. To synchronize the BRAM unit addressing with the LUT multiplexer selection, a single unit is used for simultaneous control. The obtained results show that the proposed reconfigurable 16‐tap FIR filter offers reductions of 79.3% and 74.4% of slice utilization over the hybrid variable size partitioning (VP‐Hybrid) based structure and the Radix‐2 r based structure, respectively when implemented on a Xilinx Spartan‐6 XC6SLX45 FPGA. Moreover, an improvement of efficiency is achieved compared to all reputed FPGA‐based architectures.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".