Fast, energy-efficient, robust, and reproducible mixed-signal neuromorphic classifier based on embedded NOR flash memory technology
Bibliographic record
Abstract
We have designed, fabricated, and tested a prototype mixed-signal, 28×28-binary-input, 10-ouput, 3-layer neuromorphic network based on embedded nonvolatile floating-gate cell arrays redesigned from a commercial 180-nm NOR flash memory. Each array performs a very fast and energy-efficient analog vector-by-matrix multiplication, which is the bottleneck for signal propagation in neuromorphic networks. All functional components of the prototype circuit, including 2 synaptic arrays with 101,780 floating-gate synaptic cells, 74 analog neurons, and the peripheral circuitry for weight adjustment and I/O operations, have a total area below 1 mm2. Its testing on the MNIST benchmark set has shown a classification fidelity of 94.65%, close to the 96.2% obtained in simulation. The classification of one pattern takes103× better than those of the 28-nm IBM TrueNorth digital chip for the same task at a similar fidelity. Estimates show that this performance may be further improved using a better neuron design and a more advanced memory technology, leading to a >102x advantage in speed and a >104x advantage in energy efficiency over the state-of-the-art purely digital circuits for classification of large, complex patterns. Experimental results for the chip-to-chip statistics, long-term drift, and temperature sensitivity show no evident showstoppers on the way toward practical deep neuromorphic networks with unprecedented performance.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".