AI DSP resource allocation optimizer

A learning-based algorithm is used to fine-tune DSP throughput. It learns from DSP usage spike events and attempts to better allocate DSP cycles and cache memory in order to predict and prevent them. Once trained on your song or patterns, the optimizer can reduce DSP loads by ~10% in typical cases.

The AI works in the background and training data is saved along with your song. However, the optimizations that the AI can make, are highly dependent the song as well as real-time hardware conditions that may vary between sessions, boot-ups and firmware revisions. Training data may be reset between some firmware updates, or when importing songs that saved on older firmware. Playing your song or patterns at least once after loading, will start the re-training process to optimize the performance by more intelligently allocating DSP cycles and cache memory.

Please also note that turning off Bluetooth, or using shorter delay times for delay 1, frees up more cache memory for the optimizer to allocate, and can further improve DSP throughput if DSP spikes are an issue.

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