Add 16A8W quantization configuration utility for ARM backend

[Ninja91]

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This diff implements a 16A8W (16-bit activations, 8-bit weights) quantization configuration utility for the ExecutorTorch ARM backend, following the feedback from D79746479.

Key Changes

1. New Quantization Configuration Function

• Add get_16a8w_quantization_config() in fbcode/executorch/backends/arm/quantizer/arm_quantizer.py
• Provides 16-bit activations with HistogramObserver (better precision than 8A8W)
• Maintains 8-bit weights with MinMaxObserver/PerChannelMinMaxObserver (memory efficient)
• Technically supported by TOSA through EXT-INT16 extension/profile

Benefits

• Better Precision: 16-bit activations provide higher precision than 8-bit. Useful for carrying precision for recurring neural nets.
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Differential Revision: D81550512

[pytorch-bot]

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❌ 2 New Failures, 2 Unrelated Failures

As of commit 4632410 with merge base 02da205 ():

NEW FAILURES - The following jobs have failed:

• Lint / lintrunner / linux-job (gh)
  >>> Lint for backends/arm/quantizer/arm_quantizer.py:
• pull / unittest-arm-backend-with-no-fvp (test_pytest_ops) / linux-job (gh)
  RuntimeError: Command docker exec -t 751888db88ec6e8f56e24ed07dde5d2bde7efa2011eb4fa72eb540ca4ba06461 /exec failed with exit code 1

BROKEN TRUNK - The following jobs failed but were present on the merge base:

👉 Rebase onto the `viable/strict` branch to avoid these failures

• pull / unittest-arm-backend-with-no-fvp (test_pytest_models) / linux-job (gh) (trunk failure)
  backends/arm/test/models/stable_diffusion/test_T5EncoderModel.py::TestT5EncoderModel::test_T5EncoderModel_tosa_INT
• pull / unittest-nxp-neutron / linux-job (gh) (trunk failure)
  test_split_group_convolution__applied_by_default

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