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An ESP32-S3 pedal runs neural amp models with hand-written kernels

github.com 12 min read Editor score 7/10

The pedal runs 23-layer WaveNet amp models at 48 kHz, using block floating point, hand-written Xtensa kernels, two cores, and 64-frame blocks; the same firmware also builds to WebAssembly. It is a concrete example of bringing neural inference to constrained hardware.

What: - CoyoPedal is a standalone guitar amp and effects pedal that runs full-size Neural Amp Modeler A2 captures in real time on an ESP32-S3.
How: - Its 23-layer, eight-channel WaveNet uses block floating point, hand-written Xtensa kernels, both cores, and 64-frame blocks.
Why it matters: - The same 48 kHz firmware runs on a bare ESP32-S3 module; the devkit stayed within its block budget without missed deadlines.

The diagram shows the pedal's pre-amp effects, amp, and digital delay in signal order. Sketched from the article's own text.

Read on github.com → Hacker News · 20 comments

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