An Energy-Efficient and Noise-Tolerant Recurrent Neural Network Using Stochastic Computing
Why this work is in the frame
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Bibliographic record
Abstract
Recurrent neural networks (RNNs) are widely used to solve a large class of recognition problems, including prediction, machine translation, and speech recognition. The hardware implementation of RNNs is, however, challenging due to the high area and energy consumption of these networks. Recently, stochastic computing (SC) has been considered for implementing neural networks and reducing the hardware consumption. In this paper, we propose an energy-efficient and noise-tolerant long short-term memory-based RNN using SC. In this SC-RNN, a hybrid structure is developed by utilizing SC designs and binary circuits to improve the hardware efficiency without significant loss of accuracy. The area and energy consumption of the proposed design are between 1.6%-2.3% and 6.5%-11.2%, respectively, of a 32-bit floating-point (FP) implementation. The SC-RNN requires significantly smaller area and lower energy consumption in most cases compared to an 8-bit fixed point implementation. The proposed design achieves a higher noise tolerance compared to binary implementations. The inference accuracy is from 10% to 13% higher than an FP design when the noise level is high in the computation process.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it