MLIR (Multi-Level Intermediate Representation)

Define NPU-specific Dialect in MLIR.

Lower PyTorch FX graphs into MLIR's Linalg dialect.

Write transformation passes to map Linalg to NPU instructions.

Optimize memory layout using the MemRef dialect.

Generate binary firmware for the chip.

Create a Quantum Dialect to represent qubits and gates.

Implement verification logic to ensure gate compatibility.

Apply noise-aware optimization passes in MLIR.

Lower the IR to a hardware-specific pulse sequence.

Simulate the IR before physical execution.

Import the model into MLIR via ONNX.

Use the Polyhedral dialect to fuse attention kernels.

Apply quantization passes (FP16 to INT8).

Optimize constant folding and dead code elimination.

Output optimized CUDA code for NVIDIA GPUs.

Analyze model sparsity using MLIR analysis passes.

Apply sparsity-aware optimizations.

Lower to the TOSA (Tensor Operator Set Architecture) dialect.

Generate optimized ARM Neon assembly.

Verify latency metrics using the MLIR timing framework.

Parse C/C++ code into the Affine dialect.

Identify parallel loops suitable for vectorization.

Lower Affine loops to the Vector dialect.

Apply strip-mining and unrolling optimizations.

Emit LLVM IR for final machine code generation.

Model CPU and FPGA tasks as distinct Dialects.

Analyze data dependencies using MLIR's data-flow analysis.

Insert explicit DMA (Direct Memory Access) operations in the IR.

Optimize data layout for shared memory.

Generate synchronized code for both targets.

Represent SQL operators as MLIR operations.

Apply logical optimizations (predicate pushdown).

Lower to the Vector and Linalg dialects.

Compile to optimized CPU instructions using the LLVM backend.

Execute directly on the data stream.