Comparative Study on Quantization-Aware Training of Memristor Crossbars for Reducing Inference Power of Neural Networks at The Edge
Bibliographic record
Abstract
As Internet-of- Things (IoT) technology is spreading widely in human life, a massive number of IoT sensors and edge devices generate huge amounts of unstructured data everywhere and every time. To mitigate the energy burden of computation and communication for processing these huge data at the cloud servers, edge intelligence becomes essential in IoT sensors. In this paper, for implementing edge intelligence in IoT sensors, a comparative study on the training of memristor crossbars is carried out for reducing the crossbar's inference power at the edge. For understanding the relationship of Convolutional Neural Network (CNN) architecture and crossbar's power consumption, memristor-crossbar CNNs with different synapse types, different kernel sizes, and different percentages of Low Resistance State (LRS) cells in the crossbar are compared and analyzed in this paper. After the comparative study, ternary synapse, small kernel size, and reduced number of active bits can be suggested for achieving a higher recognition rate and lower crossbar's power consumption than the other memristor-crossbar CNNs. Adjusting the percentage of LRS cells in the crossbar indicates that the recognition rate begins to fall sharply when the percentage of LRS cells becomes less than 10% of the total memristor cells, for Modified National Institute of Standards and Technology (MNIST) and Canadian Institute For Advanced Research (CIFAR-10) datasets. To minimize the recognition rate loss due to the reduction of active bits, the quantization-aware training of memristor crossbars is combined with the optimization of crossbar's inference power. Here the training with weight quantization can be repeated to minimize the recognition rate loss until the inference power consumption of memristor-crossbar CNN reaches a target inference power.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".