An Energy-Efficient Accelerator Architecture with Serial Accumulation\n Dataflow for Deep CNNs
Bibliographic record
Abstract
Convolutional Neural Networks (CNNs) have shown outstanding accuracy for many\nvision tasks during recent years. When deploying CNNs on portable devices and\nembedded systems, however, the large number of parameters and computations\nresult in long processing time and low battery life. An important factor in\ndesigning CNN hardware accelerators is to efficiently map the convolution\ncomputation onto hardware resources. In addition, to save battery life and\nreduce energy consumption, it is essential to reduce the number of DRAM\naccesses since DRAM consumes orders of magnitude more energy compared to other\noperations in hardware. In this paper, we propose an energy-efficient\narchitecture which maximally utilizes its computational units for convolution\noperations while requiring a low number of DRAM accesses. The implementation\nresults show that the proposed architecture performs one image recognition task\nusing the VGGNet model with a latency of 393 ms and only 251.5 MB of DRAM\naccesses.\n
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".