ggml: aarch64: implement SVE kernels for q2_k_q8_k vector dot

[Vithulep]

This PR introduces support for SVE (Scalable Vector Extensions) kernels for the q2_K_q8_K vector dot on the Arm architecture. A similar proposal for SVE support is made in PR 7433 and 11227.

This PR contains the SVE implementation of the vector dot used to compute the Q2_K quantization.
By running a Q2_K quantized model of mistral-7b-v01, on Graviton 3E (Perf 21 XL), Accuracy and Performance are measured.

Performance

The performance enhancement with this PR (SVE) is ~ x1.03 to x1.09 faster than the NEON implementation.

• Decoding Throughput (TPOT)

Threads	NEON (original)	This PR(SVE)	Ratio
2	4.31	4.67	1.08
4	8.43	9.17	1.09
8	16.24	17.56	1.08
16	30.04	32.24	1.07
32	50.06	53.12	1.06
48	58.05	59.78	1.03

The command used to measure the performance is

./llama-bench  -m ${PATH_TO_MODEL} -n 0 -n 16 -p 64 -t 2,4,8,16,32,48

Perplexity

I have ran perplexity with the NEON(Original) and SVE (This PR) Implementation.
And below is the summary.

NEON (original)	SVE (this PR)
3.1285 +/- 0.40252	3.1289 +/- 0.40320

This correction does not appear to have any impact on accuracy.

[ggerganov]

Use 4-space indentation in the switch cases:

Suggested change

	switch (vector_length) {
	case 128:
	for (int i = 0; i < nb; ++i) {
	const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
	svfloat32_t d_broad = svdup_n_f32((float32_t)d);
	const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
	switch (vector_length) {
	case 128:
	for (int i = 0; i < nb; ++i) {
	const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
	svfloat32_t d_broad = svdup_n_f32((float32_t)d);
	const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);