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pi-multifx-pedal/RESEARCH_FINDINGS.md
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shawn 0e77adb4c3 Build IR convolution engine 
- Full FFT overlap-add IR convolution in IRLoader (process(), set_mix(), toggle)
- Lazy FFT computation — IR FFT padded to correct block+ir size on first process()
- Wet/dry mix control, enabled/disabled toggle with tail clearing
- Fixed pipeline._apply_ir_cab() to delegate to IRLoader.process() instead of
  poking internals (old code had array-size mismatch bug: IR FFT at ir_len vs
  block FFT at conv_size)
- 46 tests: loading, convolution correctness, overlap-add state, mix, toggle,
  directory listing, performance budget (all <5ms even at 8192 taps), edge cases
- scripts/download_irs.sh: free IR pack downloader (God's Cab, Seacow)
2026-06-07 23:46:02 -04:00

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NAM A2 / A1 Inference on RPi 4B — Structured Research Findings

Date: 2026-06-07 Project: pi-multifx-pedal Context: BLOCK_SIZE=256, SAMPLE_RATE=48000, NAM v0.13.0 installed

1. NAM LV2 Plugin on aarch64

Repository: https://github.com/sdatkinson/NeuralAmpModelerPlugin (★2720, 223 forks)

  • Built with iPlug2 framework (not JUCE)
  • Build scripts: NeuralAmpModeler/scripts/ — makedist-mac.sh, makedist-win.bat
  • README: "For Linux support, there is an LV2 plugin available at mikeoliphant/neural-amp-modeler-lv2"
  • No Linux build CI exists — GitHub Actions only build for macos-latest and windows-latest

Prebuilt binaries

  • None for ARM/Linux. All releases (v0.7.6-v0.7.15) ship only macOS .dmg and Windows .zip. No Linux, no ARM.

Issues mentioning ARM/aarch64

Issue Title State Notes
#357 [FEATURE] Arm64 support OPEN (Aug 2023) Windows ARM64 request. Maintainer asked for specifics. No resolution in 3+ years.
#587 NAM Setup — Windows 11 on ARM CLOSED Setup packaging issue, not cross-compile.
#627 fix: avoid audio-thread stalls (AU) CLOSED "arm" incidental in body (part of "harm").

Conclusion: No official aarch64 support for the NAM plugin. Issue #357 has sat open for 3+ years. The iPlug2-based plugin is not designed for cross-compilation to aarch64.

The LV2 option (NeuralAudio-based)

  • https://github.com/mikeoliphant/neural-amp-modeler-lv2 (★460, 50 forks)
  • Uses NeuralAudio C++ engine (not iPlug2)
  • Supports NAM A1 + A2, WaveNet + LSTM + ConvNet
  • NeuralAudio README explicitly claims: "internal implementation outperforms NAM Core on all tested platforms (Windows x64, Linux x64/Arm64)"
  • Build: cmake .. -DCMAKE_BUILD_TYPE=Release && make -j4
  • CMake flags of interest: -DUSE_NATIVE_ARCH=ON, -DBUILD_INTERNAL_STATIC_WAVENET=ON, -DWAVENET_FRAMES=XX
  • GitHub Actions only builds for amd64 Ubuntu + Windows — no ARM in CI
  • No prebuilt ARM binaries released, but should compile natively on RPi OS 64-bit

2. NAMR (Rust NAM)

The URL github.com/mikeoliphant/neural-amp-modeler-rs does not exist (404).

a) OpenSauce/nam-rs (Pure Rust library crate)

  • https://github.com/OpenSauce/nam-rs (June 2026, very new, ★0)
  • Pure Rust, no external C/C++ dependencies
  • Supports: WaveNet (A1 + A2 single), LSTM, SlimmableContainer (NAM A2)
  • aarch64: Supported via Cargo, verified target aarch64-unknown-linux-gnu
  • Has dedicated RPi performance analysis in issues:
    • Issue #23: "Raspberry Pi / embedded support: run the A1 WaveNet presets real-time on aarch64"
    • Concluded: A2 SlimmableContainer is the CPU/quality dial, superseding bespoke specialization
  • Performance (x86, standard WaveNet, 512 samples): ~951us for process_buffer
  • Estimated Pi 4 factor: 4-8x slower
    • At 256 samples (our project): ~1.9-3.8ms standard; Feather ~0.5-1ms
  • Published on crates.io as nam-rs. MIT license.

b) fabiohl/nam-rs (Standalone + CLAP plugin)

  • https://github.com/fabiohl/nam-rs (★4, April 2026, v1.7.0)
  • x86-64-v3 only (AVX2+FMA mandatory). No aarch64 support.
  • Standalone PipeWire + CLAP plugin. Explicitly no LV2.
  • Apache-2.0 license.

Is Rust NAM faster than Python/PyTorch?

Yes. Compiled Rust/C++ avoids Python interpreter overhead (~50-200us per call), PyTorch tensor dispatch, dynamic graph construction, and GC pauses. OpenSauce/nam-rs on x86: ~951us for 512-sample standard. Python equivalent via nam library is conservatively 3-5x slower.

3. ONNX Runtime Inference

NAM ONNX Export Status (v0.13.0)

  • nam.models.exportable.Exportable has export_onnx() method
  • ALL four model architectures (ConvNet, WaveNet, Linear, LSTM) raise NotImplementedError
  • It is a stub only — no implementation exists in any class
  • The working export path is .nam JSON format via export() (not ONNX)
  • ONNX export does not exist in NAM v0.13.0

Environment

  • PyTorch 2.12.0+cpu (installed)
  • ONNX Runtime 1.26.0 (installed but not usable without export pathway)
  • ONNX package not installed

aarch64 ONNX Runtime

Even if ONNX export were available: ONNX Runtime on aarch64 uses NEON SIMD + Arm Compute Library, but Conv1D workloads would be marginal vs PyTorch's oneDNN NEON kernels. Not worth pursuing.

4. RPi 4B Cortex-A72 NEON Performance

From OpenSauce/nam-rs (#18)

Model x86 512smp Est. Pi4 4x 512smp Est. Pi4 8x 512smp Est. Pi4 256smp
Standard (16/8ch, 10 dil) 951us 3.8ms 7.6ms 1.9-3.8ms
Lite (12/6ch) ~520us 2.1ms 4.2ms ~1-2ms
Feather (8/4ch, 7 dil) ~237us 0.95ms 1.9ms 0.5-1ms
Nano (4/2ch) ~57us 0.23ms 0.46ms ~0.1-0.2ms

Cortex-A72 constraints: 2x NEON 128-bit (vs 256-bit AVX2), 1.5GHz, no FMA, lower bandwidth.

From PiPedal (RPi OS + MOTU M2 USB) — round-trip latency

Buffer 2 periods 3 periods 4 periods
16 samples Fails 2.7ms 3.9ms
32 samples 4.6ms 4.9ms 5.7ms
64 samples 5.8ms 7.2ms 8.6ms
128 samples 9.2ms 11.9ms 14.6ms

Can a 32-channel, 4-dilation ConvNet process 256 samples in under 10ms?

Yes, easily. This is a Feather-class model. ~98K multiply-adds total. NEON at 1.5GHz: ~16us theoretical. With tanh overhead: well under 1ms.

NAM A2 Slimmable performance

Designed for this use case. Quality dial: 0.0 (narrow) to 1.0 (full). RT-safe switching. Recommended: quality=0.3-0.5 for 5.33ms block budget.

5. Existing Projects

rerdavies/pipedal (★277) — THE RPi NAM pedal

  • https://github.com/rerdavies/pipedal
  • Full guitar pedal for RPi 4/5 with web UI (phone-first design)
  • v2.0 includes NAM A2, Tone3000 integration, LV2 plugins
  • Measured round-trip latency as low as 2.7ms (16smp x 3 periods)
  • Most mature RPi NAM project — the benchmark

rerdavies/ToobAmp (★86)

Klinenator/raspi-NAM (★1)

tone-3000/nam-pedal (★38)

mikeoliphant/Stompbox (★128)

Key Recommendations

Budget: 256 samples / 48kHz = 5.33ms per block

Approach Feather Standard Plugin
Python PyTorch (current) ~3ms OK 5-10ms MARGINAL none
OpenSauce/nam-rs (Rust) ~0.5-1ms OK 1.9-3.8ms OK library
neural-amp-modeler-lv2 (C++) <0.5ms OK 1-2ms OK LV2
PiPedal (C++) <0.5ms OK 1-2ms OK LV2

Recommended path

  1. Short-term: Keep Python + PyTorch with Feather models only (current code works)
  2. Medium-term: Build neural-amp-modeler-lv2 natively on RPi for LV2 integration
  3. Long-term: Port to OpenSauce/nam-rs for pure-Rust, aarch64-native, A2 Slimmable support with runtime quality/speed dialing
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