The following intrinsics have been extended to support brain float types:
svbfloat16_t svclasta[_bf16](svbool_t pg, svbfloat16_t fallback, svbfloat16_t data)
bfloat16_t svclasta[_n_bf16](svbool_t pg, bfloat16_t fallback, svbfloat16_t data)
bfloat16_t svlasta[_bf16](svbool_t pg, svbfloat16_t op)
svbfloat16_t svclastb[_bf16](svbool_t pg, svbfloat16_t fallback, svbfloat16_t data)
bfloat16_t svclastb[_n_bf16](svbool_t pg, bfloat16_t fallback, svbfloat16_t data)
bfloat16_t svlastb[_bf16](svbool_t pg, svbfloat16_t op)
svbfloat16_t svdup[_n]_bf16(bfloat16_t op)
svbfloat16_t svdup[_n]_bf16_m(svbfloat16_t inactive, svbool_t pg, bfloat16_t op)
svbfloat16_t svdup[_n]_bf16_x(svbool_t pg, bfloat16_t op)
svbfloat16_t svdup[_n]_bf16_z(svbool_t pg, bfloat16_t op)
svbfloat16_t svdupq[_n]_bf16(bfloat16_t x0, bfloat16_t x1, bfloat16_t x2, bfloat16_t x3, bfloat16_t x4, bfloat16_t x5, bfloat16_t x6, bfloat16_t x7)
svbfloat16_t svdupq_lane[_bf16](svbfloat16_t data, uint64_t index)
svbfloat16_t svinsr[_n_bf16](svbfloat16_t op1, bfloat16_t op2)
__ARM_FEATURE_SVE_BF16 will imply __ARM_FEATURE_BF16_SCALAR_ARITHMETIC so guarding only on the former should be sufficient. Same applies below