Investigating the Impact of Imprecise Computation in Memristive Memory Arrays on the Classification Performance of Deep Fully Connected Neural Networks
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Bibliographic record
Abstract
Hardware implementation of deep neural networks (DNNs) using digital hardware accelerators suffers from the inefficiency of the frequent transfer of network parameters from external memory, such as dynamic random access memory (DRAM), to the processing unit. One attractive solution to this problem is to replace the von Neumann architecture of digital systems with the concept called in-memory computation, where the computations are performed with lower precision but inside the memory array that eliminates the need to transfer the network parameters around. However, reducing the precision of computation can degrade the performance of DNN. In this article, we study the effect of using imprecise circuits, such as analog vector-by-matrix multipliers implemented with memristive crossbars, on the classification performance of fully connected neural networks and show that how by increasing the capacity of the network, we can increase the tolerance of the network to hardware defects.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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