The scalar reference code for this matrix multiplication example is shown as follows. Note that the data is stored in columns.

```
void matmul_mat8_scalar(input_window_int16* matA,
input_window_int16* matB,
output_window_int16* matC){
for(int i=0; i<M; i++){//M=64
for(int j=0;j<L;j++){//L=2
int temp = 0 ;
for(int k=0; k<N; k++){//N=8
temp += window_read(matA)*window_readincr(matB);//B is circular buffer, size N*L
window_incr(matA,64); //Jump of 64 elements to access the next element of the same row
}
window_write(matC,(int16_t)(temp>>15)) ;
window_incr(matC,64); //Jump to the next column
}
window_incr(matA,1); //Jump of one element for moving to the next row.
window_incr(matC,1); //Jump to the next row
}
}
```

As analyzed in the previous example, Matrix Vector Multiplication, `mac16`

intrinsic is the
best choice for computing 16 lanes together because 16 int16 from a column can be loaded
at once. To compute 16 output data in a column, four `mac16`

operations are needed. The same data in vector "a" is used twice to
compute the data for two output columns. Thus, two columns of data can be loaded and two
`mac16`

used for accumulations to the two output
columns. These two loads and two MACs are repeated four times to get the results of two
output columns. This method is shown in the following pseudo-code.

```
C_[0:15,0] = A_[0:15,0:1]*B_[0:1,0]
C_[0:15,1] = A_[0:15,0:1]*B_[0:1,1]
C_[0:15,0]+= A_[0:15,2:3]*B_[2:3,0]
C_[0:15,1]+= A_[0:15,2:3]*B_[2:3,1]
C_[0:15,0]+= A_[0:15,4:5]*B_[4:5,0]
C_[0:15,1]+= A_[0:15,4:5]*B_[4:5,1]
C_[0:15,0]+= A_[0:15,6:7]*B_[6:7,0]
C_[0:15,1]+= A_[0:15,6:7]*B_[6:7,1]
```

In the previous code, each "*" denotes a MAC operation. `C_[0:15,0]`

and `C_[0:15,1]`

denote two
output columns that are accumulated separately. `A_[0:15,0:1]`

denotes the column 0 and 1, and each column has 16 elements.
`B_[0:1,0]`

denotes column 0 with 2 elements. There
will be a loop for the code in the real vectorized code because there are 64 output
rows. The `mac16`

intrinsic function to be used has the
following interface.

```
v16acc48 mac16 ( v16acc48 acc,
v64int16 xbuff,
int xstart,
unsigned int xoffsets,
unsigned int xoffsets_hi,
unsigned int xsquare,
v16int16 zbuff,
int zstart,
unsigned int zoffsets,
unsigned int zoffsets_hi,
int zstep
)
```

The buffers contain parameters (start, offsets, square, and step) to compute the indexing into buffers (vector registers). For details about the lane addressing scheme with these parameters, see MAC Intrinsics.

Note that the `mac16`

intrinsic function
prototype is different with the one introduced in the previous matrix vector
multiplication example. The `xbuff`

here is `v64int16`

which allows two sets of data to be stored and used in an
interleaved way.

Coding with MAC intrinsics can be seen in the following section.