A Stochastic Computational Multi-Layer Perceptron with Backward Propagation
Bibliographic record
Abstract
Stochastic computation has recently been proposed for implementing artificial neural networks with reduced hardware and power consumption, but at a decreased accuracy and processing speed. Most existing implementations are based on pre-training such that the weights are predetermined for neurons at different layers, thus these implementations lack the ability to update the values of the network parameters. In this paper, a stochastic computational multi-layer perceptron (SC-MLP) is proposed by implementing the backward propagation algorithm for updating the layer weights. Using extended stochastic logic (ESL), a reconfigurable stochastic computational activation unit (SCAU) is designed to implement different types of activation functions such as the tanh and the rectifier function. A triple modular redundancy (TMR) technique is employed for reducing the random fluctuations in stochastic computation. A probability estimator (PE) and a divider based on the TMR and a binary search algorithm are further proposed with progressive precision for reducing the required stochastic sequence length. Therefore, the latency and energy consumption of the SC-MLP are significantly reduced. The simulation results show that the proposed design is capable of implementing both the training and inference processes. For the classification of nonlinearly separable patterns, at a slight loss of accuracy by 1.32-1.34 percent, the proposed design requires only 28.5-30.1 percent of the area and 18.9-23.9 percent of the energy consumption incurred by a design using floating point arithmetic. Compared to a fixed-point implementation, the SC-MLP consumes a smaller area (40.7-45.5 percent) and a lower energy consumption (38.0-51.0 percent) with a similar processing speed and a slight drop of accuracy by 0.15-0.33 percent. The area and the energy consumption of the proposed design is from 80.7-87.1 percent and from 71.9-93.1 percent, respectively, of a binarized neural network (BNN), with a similar accuracy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".