Add initial TileArray programming model and Neura compiler flow

[ShangkunLi]

Summary

This PR establishes the initial Synapse programming model and compiler flow for explicitly placed programs on a CGRA tile array.

It introduces a Python TileArray DSL, records typed tile-level operations, lowers them through the MLIR Python bindings into a single Taskflow task containing a Neura kernel, and invokes the Amoeba/Neura backend to produce mapped Neura IR.

Key Changes

• Add the initial Python frontend and AST parser.
• Add the user-facing TileArray spatial abstraction using Neura-compatible (x, y) coordinates.
• Add a typed TileArray program representation with initial i32 and f32 support.
• Add frontend operations for placed constants and additions.
• Lower a captured TileArray program into:

func.func
  taskflow.task
    neura.kernel
      placed Neura operations

• Generate Neura placement attributes directly from Tile coordinates.
• Add a compiler driver that runs:
  • --leverage-predicated-value
  • --insert-data-mov
  • template mapping in spatial-only mode
• Return the final mapped IR separately from backend diagnostic output.
• Replace the standalone Neura dependency with a pinned Amoeba submodule, with Neura managed by Amoeba.
• Add tests for frontend parsing, TileArray semantics, program recording, pre-mapping IR, and final mapped IR.
• Reorganize Python tests into frontend, language, and compiler directories.
• Expand the README with LLVM, Amoeba, Python binding, build, and test instructions.
• Add CI jobs for:
  • Python 3.10 and 3.11 frontend/language tests.
  • Python 3.11 compiler integration using a pinned LLVM revision and locally built Amoeba/Neura bindings.

Current Compiler Flow

Python TileArray program
  → TileArrayProgram
  → Taskflow + placed Neura IR
  → predicated Neura values
  → inserted data movement
  → template mapping
  → mapped tiles, links, registers, and time steps

Current Scope

This PR intentionally targets the first single-task milestone:

• one implicit Taskflow task;
• one Neura kernel;
• explicitly placed TileArray operations;
• constants and additions;
• i32 and f32 scalar values.

Task-level syntax, multiple tasks, task dependencies, inter-core communication, automatic template selection, and general structured lowering remain future work.

Testing

The focused local test suite passes:

9 passed

Run it with:

python -m pytest -q \
  tests/python/frontend \
  tests/python/language \
  tests/python/compiler

The compiler tests require the pinned LLVM/MLIR build and the Amoeba Python bindings described in the README.