A Reconfigurable “ SFMD Architecture ” For a Class of Signal Processing Applications
Bibliographic record
Abstract
The fastest programmable DSP processors are unable to meet the speed requirements of many advanced signal processing applications. SlMD machines have been a preferred solution in such applications because of their inherent spatial parallelism. In such machines, a control unit (CU) broadcasts simple machine instructions simultaneously to a number of processing elements (PEs) executing the same instruction on different data The performance of such architectures can be vastly enhanced if the PEs can execute at the level of signal processing function rather than low level machine instruction. This can be made possible if the PEs are so designed that they can receive and execute functional level instruction from the CU instead of simple machine level instruction. FPGAs have emerged as high performance flexible hardware for many signal processing applications but they are not optimised for any particular application. Hence, they can not offer highest possible performance at lowest silicon cost for a given signal processing algorithm. This paper addresses these issues by introducing a new reconfigurable DSP processor, "single function multiple data (SFMD)" which eliminates the drawbacks of conventional SIMD machines and offers a balance between flexibility, reconfiguration latency and performance
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".