A MEMETIC ALGORITHM FOR PERFORMING MEMORY ASSIGNMENT IN DUAL-BANK DSPS
Bibliographic record
Abstract
To increase memory bandwidth, many programmable Digital-Signal Processors (DSPs) employ two on-chip data memories. This architectural feature supports higher memory bandwidth by allowing multiple data memory accesses to occur in parallel. Exploiting dual memory banks, however, is a challenging problem for compilers. This, in part, is due to the instruction-level parallelism, small numbers of registers, and highly specialized register capabilities of most DSPs. In this paper, we present a new methodology based on a Memetic Algorithm (MA) for assigning data to dual-bank memories. Our approach is global, and integrates several important issues in memory assignment within a single model. Special effort is made to identify those data objects that could potentially benefit from an assignment to a specific memory, or perhaps duplication in both memories. Our computational results show that the MA is able to achieve a 54% reduction in the number of memory cycles and a reduction in the range of 7%–42% in the total number of cycles when tested with well-known DSP kernels and applications. Our computational results also show that, when compared with the Genetic Algorithm in Ref. 3, the memetic algorithm is able to find solutions that, on average, have 7%–20% less cost, with the biggest improvements being found for larger problem instances.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".