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CMSIS-NN is a software library of efficient neural network kernels for Arm Cortex-M CPUs designed to maximize performance and minimize memory usage. It provides kernels across convolution, activation, fully-connected, SVDF, pooling, softmax, and basic math, with support for different data types (q7_t, q15_t, and s8) and processor-specific implementations (DSP and MVE). The library differentiates legacy 8-bit symmetric quantization APIs (_q7/_q15) from TensorFlow Lite-compatible 8-bit APIs (_s8), includes examples, and uses preprocessor macros to tailor builds for target hardware.
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