Problem

MISSING: Graph-level optimizations that fuse operators and optimize computational graphs for faster inference.

Existing

• Issue ONNX Export and ONNX Runtime (CPU/DirectML) Execution #280: ONNX export (complementary)
• Issue Inference Optimizations – KV Cache, RoPE, and FlashAttention #277: Some inference optimizations

Missing Implementations

Operator Fusion (CRITICAL):

• Conv + BatchNorm + ReLU fusion
• Matmul + Bias + Activation fusion
• Elementwise operation fusion
• Multi-headed attention fusion

Graph Optimization (CRITICAL):

• Constant folding
• Dead code elimination
• Common subexpression elimination
• Layout optimization (NCHW vs NHWC)

Memory Optimization (HIGH):

• In-place operations
• Memory reuse
• Gradient checkpointing integration
• Activation memory planning

Computation Optimization (HIGH):

• Algebraic simplification
• Strength reduction
• Loop fusion
• Vectorization hints

Frameworks to Compete With

• TensorRT (NVIDIA)
• TorchScript optimization
• ONNX Runtime optimizations
• TVM/Apache TVM

Architecture

Success Criteria

• 2-5x inference speedup from fusion
• Reduced memory footprint
• Integration with existing models
• Benchmarks vs TensorRT/ONNX Runtime