Add function (task)-level hardware target assignment pass for heterogeneous computing

[YanzhouTang]

Summary

This PR introduces a new pass AssignTaskTarget that operates at a higher abstraction level than existing partitioning mechanisms. It assigns hardware targets (CPU, CGRA, DOE) to compute functions before they are lowered to taskflow operations, enabling coarse-grained workload partitioning in heterogeneous computing systems.

Motivation

The existing PartitionTaskByTarget pass operates at the taskflow level (loop-to-CGRA mapping), which is fine-grained for certain use cases. We need a higher-level pass that can:

1. Partition at function granularity: Assign entire computational functions to different hardware units
2. Early hardware mapping: Make hardware decisions at the Linalg/func level before lowering to lower-level IRs
3. Enable heterogeneous orchestration: Support scenarios where different components run on different hardware accelerators (Now we support NeRF algorithm)

Changes

New Pass: AssignTaskTarget

• Location: lib/Conversion/AssignTaskTarget/
• Functionality:
  • Analyzes function names and assigns target.device attributes
  • Supports CPU, CGRA, and DOE (Data Orchestration Engine) hardware targets
  • Uses pattern matching for initial implementation (extensible to more sophisticated analysis)
• Usage: mlir-neura-opt --assign-task-target input.mlir

Example transformation:
// Before
func.func @hash_encoder_func(...) { ... }

// After
func.func @hash_encoder_func(...) attributes {target.device = "doe"} { ... }

[tancheng]

what is the principle to determine which accelerator should be assigned to which kernel?