Heterogeneous Distributed SRAM Configuration for Energy-Efficient Deep CNN Accelerators
Bibliographic record
Abstract
Convolutional Neural Networks (CNNs) are often the first choice for visual recognition systems due to their high, even superhuman, recognition accuracy. The memory configuration of CNN accelerators highly impacts their area and energy efficiency, and employing on-chip memories such as SRAMs is unavoidable. SRAMs can reduce the number of energy-hungry DRAM accesses by storing a large amount of data locally. In this paper, we propose a new on-chip memory configuration, for a certain class of CNN accelerators that divides the memories into two groups. The first group consists of shallow but wide SRAMs into which parallel computational units accumulate intermediate results. The second group includes narrow but deep SRAMs shared between adjacent computational units to store then transfer final results to the external DRAM without interrupting the computation process. Implementation results show that the proposed configuration reduces the area by 21 % and improves the energy efficiency by 18% compared to designs which use an ordinary ping-pong structure for SRAM-DRAM data transfer.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".