Add MNIST-FC inference layer examples on NPU2

[erwei-xilinx]

Summary

• Add programming_examples/mnist_fc/ with standalone examples for GGML MNIST-FC inference pipeline layers: broadcast bias add, ReLU (f32 via bf16 cmp/sel), and argmax (scalar reduction)
• Add integration test that chains matmul(500x500x784) + fused bias+relu + bias_add + argmax in a single 4-launch multi-launch module on NPU2 (Strix, AIE2P)
• Update operator dashboard with new "ML Pipeline" category entries

Test plan

• All 4 sub-examples pass on NPU2 hardware (make run in each directory)
• Integration test verifies end-to-end argmax output against numpy golden reference (exact i32 match)
• Operator dashboard regenerated with NPU2 🟢 status for all MNIST-FC entries
• Lit tests configured for ryzen_ai_npu2, peano

🤖 Generated with Claude Code

[Copilot]

Pull request overview

Adds a new programming_examples/mnist_fc/ set of NPU2-focused examples that demonstrate key MNIST-FC inference pipeline layers (broadcast bias add, ReLU, argmax) plus an integration module that composes multiple launches, and updates the operator dashboard to surface them under a new “ML Pipeline” category.

Changes:

• Add standalone MNIST-FC layer examples (broadcast bias add, 2D ReLU, argmax) with Makefile + LIT runners for NPU2/Peano.
• Add an MNIST-FC integration example that extends the existing test54 matmul module with additional element-wise launches.
• Update the programming examples operator dashboard entries and regenerate programming_examples/README.md.

Reviewed changes

Copilot reviewed 14 out of 14 changed files in this pull request and generated 5 comments.

Show a summary per file

File	Description
programming_examples/mnist_fc/relu/run_npu2_peano.lit	Adds NPU2/Peano LIT runner for the ReLU example.
programming_examples/mnist_fc/relu/run.py	Implements 2D ReLU layer example using bf16 compare/select path on NPU2.
programming_examples/mnist_fc/relu/Makefile	Adds build/run targets for the ReLU example.
programming_examples/mnist_fc/integration/run_npu2_peano.lit	Adds NPU2/Peano LIT runner for the integration example.
programming_examples/mnist_fc/integration/run.py	Builds a multi-launch integration module (matmul + element-wise stages) and validates argmax vs golden reference.
programming_examples/mnist_fc/integration/Makefile	Adds build/run targets for the integration example.
programming_examples/mnist_fc/broadcast_bias_add/run_npu2_peano.lit	Adds NPU2/Peano LIT runner for broadcast bias add.
programming_examples/mnist_fc/broadcast_bias_add/run.py	Implements broadcast bias add layer example aligned to GGML-style layout semantics.
programming_examples/mnist_fc/broadcast_bias_add/Makefile	Adds build/run targets for broadcast bias add.
programming_examples/mnist_fc/argmax/run_npu2_peano.lit	Adds NPU2/Peano LIT runner for argmax.
programming_examples/mnist_fc/argmax/run.py	Implements row-wise argmax example (scalar reduction) with sampling-based verification.
programming_examples/mnist_fc/argmax/Makefile	Adds build/run targets for argmax.
programming_examples/generate_readme.py	Registers new MNIST-FC examples in the operator dashboard generation list.
programming_examples/README.md	Regenerates the operator dashboard table to include new “ML Pipeline” entries.

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[Copilot]

l1_tile_relu_bf16/l1_tile_relu_bf16 is allocated but never used (and sub_relu_bf16 is created but unused). This is dead code and wastes L1 memory; either remove these allocations/subviews or actually write the ReLU bf16 result into them if you intended to materialize the intermediate.

Suggested change

l1_tile_relu_bf16 = AllocOp(l1TileTy_bf16, [], [])

[Copilot]

The file-level pipeline description says the integration test chains matmul1 -> bias_add+relu -> bias_add2 -> argmax, but the module currently does not compute matmul2 and bias_add2/argmax operate on a host-provided matmul2_out buffer. This makes the “end-to-end MNIST-FC” claim misleading; either add matmul2 into the module (and feed bias_add2 from the relu output) or update the description/PR summary to clearly state what is (and isn’t) validated.

[Copilot]

truncf_op and block_matmul are imported from test54 but never referenced in this file. If they aren’t required for side effects, remove the unused imports to avoid confusion about dependencies.

Suggested change

	truncf_op,
	block_matmul,

[Copilot]

bfloat16 is imported but never used in this example. Please remove the unused import to keep the script minimal.

Suggested change

from ml_dtypes import bfloat16

[Copilot]

matmul_ir = str(matmul_module) is assigned but never used (the subsequent comments also describe approaches that aren’t implemented). Please remove the unused variable/commented-out approach or complete the implementation so the code reflects the chosen strategy.

Suggested change

	# ── Phase 2: serialize vectorized matmul, extend with element-wise ──
	# Get the matmul function's IR, then rebuild a module that includes
	# both the matmul launch and the element-wise launches.
	matmul_ir = str(matmul_module)
	# Parse the vectorized matmul module, then add element-wise launches
	# by modifying the function to accept additional arguments.
	# Strategy: re-parse the matmul IR, add extra func arguments and
	# append element-wise launches into the function body.
	# We need to extend the function signature. The matmul func has:
	# func @matmul_f32(%A, %B, %C) -> ()
	# We need to change it to:
	# func @mnist_fc(%A, %B, %C, %bias1, %relu_out, %mat2_out, %bias2, %bias2_out, %argmax_out) -> ()
	# And add the element-wise launches after the matmul launch.
	# Simplest approach: rebuild from scratch with the matmul IR as a string
	# embedded in the new module. But this is fragile.
	# Better approach: use the matmul module directly, modify its function
	# to add more arguments and more launches.
	# ── Phase 2: extend vectorized matmul with element-wise launches ──
	# We need to extend the function signature. The matmul func has:
	# func @matmul_f32(%A, %B, %C) -> ()
	# We need to change it to:
	# func @mnist_fc(%A, %B, %C, %bias1, %relu_out, %mat2_out, %bias2, %bias2_out, %argmax_out) -> ()
	# And add the element-wise launches after the matmul launch.
	# Use the matmul module directly, modifying its function to add more
	# arguments and additional launches instead of reparsing IR as a string.