Software-Level Accuracy Using Stochastic Computing With Charge-Trap-Flash Based Weight Matrix
Bibliographic record
Abstract
The in-memory computing paradigm with emerging memory devices has been recently shown to be a promising way to accelerate deep learning. Resistive processing unit (RPU) has been proposed to enable the vector-vector outer product in a crossbar array using a stochastic train of identical pulses to enable one-shot weight update, promising intense speed-up in matrix multiplication operations, which form the bulk of training neural networks. However, the performance of the system suffers if the device does not satisfy the condition of linear conductance change over around 1,000 conductance levels. This is a challenge for nanoscale memories. Recently, Charge Trap Flash (CTF) memory was shown to have a large number of levels before saturation, but variable non-linearity. In this paper, we explore the trade-off between the range of conductance change and linearity. We show, through simulations, that at an optimum choice of the range, our system performs nearly as well as the models trained using exact floating point operations, with less than 1% reduction in the performance. Our system reaches an accuracy of 97.9% on MNIST dataset, 89.1% and 70.5% accuracy on CIFAR-10 and CIFAR-100 datasets (using pre-extracted features). We also show its use in reinforcement learning, where it is used for value function approximation in Q-Learning, and learns to complete an episode the mountain car control problem in around 146 steps. Benchmarked to state-of-the-art, the CTF based RPU shows best in class performance to enable software equivalent performance.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".