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Definitely a coming area. At present it's only for PC GPUs but I imagine we'll soon see it on high end MCUs.</div>
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<a href="https://www.gpu.audio/">https://www.gpu.audio/</a></div>
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<b>From:</b> Synth-diy <synth-diy-bounces@synth-diy.org> on behalf of Eric Brombaugh via Synth-diy <synth-diy@synth-diy.org><br>
<b>Sent:</b> 24 April 2026 19:45<br>
<b>To:</b> Synth-diy@synth-diy.org <synth-diy@synth-diy.org><br>
<b>Subject:</b> [sdiy] ML / AI for music & synthesis (not coding!)</div>
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<div style="font-size: 11pt;">A more interesting topic to me is the use of machine learning and AI<br>
methods for realtime synthesis / effects and music creation. More and<br>
more often these days I'm seeing IoT / edge computing hardware that has<br>
neural processing accelerators built in. Often these are in quite<br>
inexpensive parts - SoCs targeted at networked cameras w/ machine vision<br>
features, MCUs with audio processing for "wake up word" recongnition,<br>
etc. So the question is how can these resources be turned to interesting<br>
synthesis applications. Often these accelerators are merely fast linear<br>
algebra coprocessors that do fast dot products so that would easily<br>
apply to filtering and convolution, but often they're limited to 8-bit<br>
data so unless you're doing chiptunes stuff that's not so useful. Any<br>
other cool ways to apply them?<br>
<br>
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