[Bug] [RISC-V RVV] Performance Issue: bias_add operator slower with vectorization

### Issue: [RISC-V RVV] Performance Issue: bias_add operator slower with vectorization

#### Description
The bias_add operator shows significant performance degradation when using the RISC‑V Vector (RVV) extension. With an acceleration ratio of 0.360, the RVV implementation is nearly 3× slower than the scalar implementation. This is unexpected for a channel‑wise addition operation that should benefit from vectorization.

#### Steps to Reproduce
1. Generate the bias_add operator with the following configuration:
```python
params = {
"dtype": "float32",
"batch": 14,
"channels": 23,
"input_height": 67,
"input_width": 99
}
```

2. Export the operator to two targets:
- **RV target** (scalar, without vector extension):
```
llvm -mtriple=riscv64-linux-gnu -mcpu=generic-rv64 -mabi=lp64d -mattr=+64bit,+m,+a,+f,+d,+c
```
- **RVV target** (with vector extension):
```
llvm -mtriple=riscv64-linux-gnu -mcpu=generic-rv64 -mabi=lp64d -mattr=+64bit,+m,+a,+f,+d,+c,+v
```

3. Run performance measurement on both targets.

Operator definition code:
```python
def export_bias_add(params, set_dir=None, platform="rv"):
data = relay.var("data",
shape=(params["batch"], params["channels"],
params["input_height"], params["input_width"]),
dtype=params["dtype"])
bias = relay.var("bias", shape=(params["channels"],), dtype=params["dtype"])
bias_add = relay.nn.bias_add(data, bias)
export_op(bias_add, params["op_name"], [data, bias], params, set_dir=set_dir)
```

#### Performance Data
- **RV execution time**: 7.683920 ms
- **RVV execution time**: 21.363800 ms
- **Acceleration ratio (RV/RVV)**: 0.360 (RVV is ~2.8× slower)

#### Environment Information
- **TVM version**: 0.19.0
- **LLVM version**: [Please provide: `llvm-config --version`]
- **Hardware**: Spacemit K1‑X bit‑brick board
- **CPU**: Spacemit X60 (8 cores, 1.6 GHz)
- **ISA**: rv64imafdcv (with vector extensions)
- **Memory**: 7.6 GB
- **OS**: Bianbu 2.2, Linux kernel 6.6.63
- **Operation**: Channel‑wise bias addition on a tensor of shape (14, 23, 67, 99)

#### Expected Behavior
RVV vectorization should provide a performance improvement over the scalar RV baseline for broadcast addition operations like bias_add.

#### Additional Context
- The bias_add operation adds a 1D bias vector to each channel of a 4D tensor (≈1.7M elements total).
- The performance regression is severe and similar to other operators (sum, log, relu, etc.).
- This suggests that the current RVV vectorization for broadcast operations may be suboptimal, or there are inefficiencies in memory access patterns or instruction selection.

Start with the export_bias_add entry point, relay.nn.bias_add, and export_op using the provided tensor shape and RV/RVV LLVM target configurations. Reproduce the measurements on the Spacemit K1-X, then trace the generated RVV execution for broadcast addition; done means identifying and correcting the cause of the regression and confirming RVV outperforms the scalar baseline.

Written by the indexing model from the issue text.