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Result

64.66% test accuracy on SHD after 100 epochs. Run date: 2026-07-09. Architecture: feedforward LIF, 2 hidden layers, no recurrence.
This is a credibility result, not a state-of-the-art claim. The goal is an honest baseline that demonstrates the implementation is correct, not a maximized benchmark number.

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Architecture

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Published reference comparisons

The committed result (64.66%) falls within the Zheng et al. “without conditions” reference band (63.2–74.8%), confirming the surrogate-gradient LIF implementation is correct for sparse event-based inputs. All recurrent-network SHD results (≥ 71.4%) are excluded — recurrent vs. feedforward is not a valid comparison for this architecture.

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Epoch curve

Rapid initial learning (epochs 1–14) followed by a noisy plateau, characteristic of feedforward LIF on SHD: the small training set (8,156 samples) and absence of regularisation limit further gains. The compiled artifact at templates/keyword-spotting/model.thx uses the epoch-14 checkpoint.

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Floor assessment

PASS — 64.66% exceeds the committed ≥60.0% floor by 4.66 percentage points. The floor was set with conservative margin below the Zheng et al. 2025 reference band. The result sits within that band.

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Hardware and software

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Reproducing

The training script is committed at templates/keyword-spotting/train.py and is reproducible from seed 0 on any device with PyTorch.