Modified Hotspot Cache Architecture: A Low Energy Fast Cache for Embedded Processors
Bibliographic record
Abstract
The cache memory plays a crucial role in the performance of any processor. The cache memory (SRAM), especially the on chip cache, is 3-4 times faster than the main memory (DRAM). It can vastly improve the processor performance and speed. Also the cache consumes much less energy than the main memory. That leads to a huge power saving which is very important for embedded applications. In today's processors, although the cache memory reduces the energy consumption of the processor, however the energy consumption in the on-chip cache account to almost 40% of the total energy consumption of the processor. In this paper, we propose a cache architecture, for the instruction cache, that is a modification of the hotspot architecture. Our proposed architecture consists of a small filter cache in parallel with the hotspot cache, between the L1 cache and the main memory. The small filter cache is to hold the code that was not captured by the hotspot cache. We also propose a prediction mechanism to steer the memory access to either the hotspot cache, the filter cache, or the L1 cache. Our design has both a faster access time and less energy consumption compared to both the filter cache and the hotspot cache architectures. We use Mibench and Mediabench benchmarks, together with the simplescalar simulator in order to evaluate the performance of our proposed architecture and compares it with the filter cache and the hotspot cache architectures. The simulation results show that our design outperforms both the filter cache and the hotspot cache in both the average memory access time and the energy consumption
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".