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All energy values on this page are modeled from operation counts and published per-operation coefficients. They have not been measured on physical hardware. Silicon-measured power requires the M6 bring-up. See METHODOLOGY.md for full coefficient provenance.

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Task

SHD keyword spotting — the same model and samples used in the SHD benchmark.
Run date: 2026-07-16. Hardware: Apple M4 Pro (CPU, single sample, single thread).

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Energy results

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SNN via thrindex

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Dense MLP baselines

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Energy ratios vs SNN

The apples-to-apples comparison is Baseline B (temporal MLP). Both systems process 100 timesteps of temporal data from the same input. The SNN triggers only 3.7M syn-ops (9.9% firing) vs 36.9M MACs (100% dense) — a 10× reduction in operations at the same information budget. At 0.5 pJ/syn-op (neuromorphic) vs 2.56 pJ/MAC (GPU), the combined advantage is 51.5× on modeled energy.

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Why rate-collapsed MLP is not a fair baseline

Baseline A processes the mean spike rate across 100 timesteps — a single 700-dimensional vector. The timing information in the spike pattern is discarded. This approach:
  • Cannot distinguish two inputs with the same rate but different temporal structure
  • Would achieve significantly lower accuracy on SHD than the SNN (SHD specifically rewards temporal processing)
  • Uses 100× fewer operations by throwing away 99% of the input data
A rate-collapsed MLP with equivalent accuracy would require a substantially larger architecture.

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Latency note

The SNN simulator wall time (14.2 ms) reflects the thrindex CPU simulation tool — a debug and validation artifact, not the deployment target. Neuromorphic silicon operates at hardware speed, typically 10–100× faster than an equivalent CPU simulation. The MLP wall times (Baseline A: 0.012 ms, Baseline B: 1.18 ms) are measured on the same CPU for a consistent comparison point. No latency comparison between neuromorphic hardware and GPU is made here. That comparison requires M6 silicon bring-up.

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Reproducing

Writes machine-readable results to benchmarks/results/shd.json. No external dataset download required — the three SHD samples are bundled in the repository.

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Energy formula

where syn_ops = multiply-accumulates actually triggered by spikes (not the full dense count). See the energy model page for formula derivation and the Benchmarks methodology for coefficient provenance.