CTT-Based Scalable Neuromorphic Architecture
Why this work is in the frame
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Bibliographic record
Abstract
A novel spiking neuromorphic architecture is presented in this paper. The architecture is based on charge-trap transistors (CTTs) which are experimentally-verified compute-in-memory devices. The proposed low-power scalable architecture targets large neural network applications, such as machine learning tasks and emulation of brain connectivity networks. Data within the proposed architecture is encoded using a number of spikes approach. The CTT-based synapses receive Gaussian spikes, the most energy-efficient waveform for communication, as inputs from other neurons, the spikes are multiplied by synaptic weights and accumulated. The neuron, designed using a leaky integrate and fire model, generates a similar spike at the output. The proposed architecture is compared to literature and exhibits superior parameters. The neuron (including the synaptic array) occupies an area of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$178.25~\mu \text{m}^{2}$ </tex-math></inline-formula> , supporting 5.6k neurons and 560k synapses per mm2, as well as exhibits low energy per synaptic operation of 8 pJ. To validate the proposed architecture, a single neuron was designed and evaluated as a binary classifier for two numbers from the MNIST data set. The accuracy, recall, and precision of the hardware neuron for the binary classification task are, respectively, 99.2%, 99.5%, and 98.6% (similar to results from other reported works).
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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 | 0.001 |
| 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