Elevator pitch: Compile ONNX models to portable C with planned memory and BLAS speed.

Converts ONNX graphs into optimized C. plans a single tensor arena, and emits C that calls cblas_sgemm for large matmuls and a tight kernel for small ones. Writes into outputs in place when safe. A Python tester validates against ONNX Runtime and reports accuracy and speed.

Jake Errington(For telling me do this) and Christophe Dubach(For posting the COMP520 outline in advance)

1. Importer
   • Reads ONNX. Normalizes float compute paths to f32.
   • Lower GEMM computations into Matmul and Adds when possible.
2. IR
   • Initial IR was planned out with a dataclass.
   • JSON with values (dtype, shape), ops, consts, and explicit entry I/O.
3. Passes
   • Canonicalize, DCE, shape and dtype checks, topo sort(only support DAGs for now).
   • Memory planner: first-fit packs live intervals into a single arena;
4. Codegen
   • Emits kernel_matmul, kernel_add, kernel_relu, plus a cblas_sgemm fast path.
   • Embeds weights as static const float[]. Computes directly into output buffers when possible.

// Small-matmul kernel; large cases call cblas_sgemm.
static void kernel_matmul(const float* A,const float* B,float* C,int M,int K,int N){
  for(int i=0;i<M;++i){
    for(int j=0;j<N;++j) C[i*N+j]=0.f;
    for(int k=0;k<K;++k){
      float a=A[i*K+k];
      for(int j=0;j<N;++j) C[i*N+j]+=a*B[k*N+j];
    }
  }
}

Run demo.py