Fully support mv2/ mv3 with fused ops + cmsis-integration on FVP

[psiddh]

🚀 The feature, motivation and pitch

Infer / Run mv2 and mv3 on FVP with fused ops + cmsis-nn integration.

To fully support it, we need to support the following essential ops

1. Op Support

Quantized Arithmetic Operations
cortex_m::quantized_add.out — Element-wise addition using ARM CMSIS-NN function arm_elementwise_add_s8()
cortex_m::quantized_mul.out — Element-wise multiplication

Neural Network Layers / MatMul
cortex_m::quantized_conv2d.out — Convolution using arm_convolve_s8()
cortex_m::quantized_linear_per_tensor_out — Fully connected layer using arm_fully_connected_s8()
cortex_m::quantized_avg_pool2d.out — Average pooling
cortex_m::quantized_max_pool2d.out — Max pooling

• Convolution (conv2d)
• Depthwise Convolution (depthwise_conv2d)

Activation Functions
cortex_m::quantized_relu.out — ReLU activation
cortex_m::quantized_hardtanh.out — Clamped activation function

2. E2E integration of running mobile vision models

3. Performance full fledged Analysis w.r.t TFLM and Benchmarking

Alternatives

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Additional context

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RFC (Optional)

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