How can I further reduce memory usage for this microcontroller vision model? #6
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I’m running this classifier on a resource-constrained microcontroller and I’m close to the memory limit. What techniques beyond standard quantization would you recommend to reduce RAM and flash usage while keeping acceptable accuracy? |
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Replies: 2 comments
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Beyond int8 quantization, the biggest memory savings usually come from reducing input resolution or channels, using depthwise separable convolutions, replacing fully connected layers with global average pooling, and trimming the widest layers to lower peak activation memory. |
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You can further reduce memory by converting inputs to grayscale, downsizing early feature maps with stride or pooling, and minimizing activation buffer size by lowering the maximum number of channels in intermediate layers. |
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Beyond int8 quantization, the biggest memory savings usually come from reducing input resolution or channels, using depthwise separable convolutions, replacing fully connected layers with global average pooling, and trimming the widest layers to lower peak activation memory.