Path Balancing for Reducing Dynamic Power Consumption in Digital Designs Containing IP-Blocks
Bibliographic record
Abstract
When paths between computational elements of a digital design do not have the same propagation delay, then signals at the inputs of one of these computational elements could arrive at different moments. Signals arriving early than necessary can create switching activities in the computational element if no action is taken. This leads to consuming power for useless computation. One approach to overcome this situation is to balance the length of all paths using strategies like gate resizing and/or voltage scaling. However, when the computational elements are IP Blocks (blocks from a third party), then the designer is not allowed to optimize inside the computational elements to make paths balanced; instead, buffers can be inserted in some paths while making all the paths of equal length. Inserting buffers will not change the design’s functionality since signals at the input and output of a buffer are the same. We propose an Integer Linear Programming to this problem, which allows inserting a minimal number of buffers, and more than one buffer can be inserted in any path compared to existing approaches. Also, in this paper, computational elements can have different execution delays to produce their output bits, which is not the case in published approaches. Our proposed approach solves the problem for both combinational and clocked sequential digital designs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".