XNOR-RRAM: A scalable and parallel resistive synaptic architecture for binary neural networks
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Bibliographic record
Abstract
Recent advances in deep learning have shown that Binary Neural Networks (BNNs) are capable of providing a satisfying accuracy on various image datasets with significant reduction in computation and memory cost. With both weights and activations binarized to +1 or -1 in BNNs, the high-precision multiply-and-accumulate (MAC) operations can be replaced by XNOR and bit-counting operations. In this work, we propose a RRAM synaptic architecture (XNOR-RRAM) with a bit-cell design of complementary word lines that implements equivalent XNOR and bit-counting operation in a parallel fashion. For large-scale matrices in fully connected layers or when the convolution kernels are unrolled in multiple channels, the array partition is necessary. Multi-level sense amplifiers (MLSAs) are employed as the intermediate interface for accumulating partial weighted sum. However, a low bit-level MLSA and intrinsic offset of MLSA may degrade the classification accuracy. We investigate the impact of sensing offsets on classification accuracy and analyze various design options with different sub-array sizes and sensing bit-levels. Experimental results with RRAM models and 65nm CMOS PDK show that the system with 128×128 sub-array size and 3-bit MLSA can achieve accuracies of 98.43% for MLP on MNIST and 86.08% for CNN on CIFAR-10, showing 0.34% and 2.39% degradation respectively compared to the accuracies of ideal BNN algorithms. The projected energy-efficiency of XNOR-RRAM is 141.18 TOPS/W, showing ~33X improvement compared to the conventional RRAM synaptic architecture with sequential row-by-row read-out.
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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