CTT-Based Scalable Neuromorphic Architecture
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$178.25~\mu \text{m}^{2}$, 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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".