- static_assert(InputDimensions % 4 == 0);
-
-#if defined (USE_AVX512)
- if constexpr (PaddedInputDimensions % (SimdWidth * 2) != 0)
- {
- constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth;
- const auto inputVector256 = reinterpret_cast<const __m256i*>(input);
-
- __m256i sum0 = _mm256_setzero_si256();
- const auto row0 = reinterpret_cast<const __m256i*>(&weights[0]);
-
- for (int j = 0; j < (int)NumChunks; ++j)
- {
- const __m256i in = inputVector256[j];
- m256_add_dpbusd_epi32(sum0, in, row0[j]);
- }
- output[0] = m256_hadd(sum0, biases[0]);
- }
- else
-#endif
- {
-#if defined (USE_AVX512)
- constexpr IndexType NumChunks = PaddedInputDimensions / (SimdWidth * 2);
-#else
- constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth;
-#endif
- vec_t sum0 = vec_setzero();
- const auto row0 = reinterpret_cast<const vec_t*>(&weights[0]);
-
- for (int j = 0; j < (int)NumChunks; ++j)
- {
- const vec_t in = inputVector[j];
- vec_add_dpbusd_32(sum0, in, row0[j]);
- }
- output[0] = vec_hadd(sum0, biases[0]);
- }
- }
-
-#else
-
-// Use old implementation for the other architectures.
-
- auto output = reinterpret_cast<OutputType*>(buffer);
-
-#if defined(USE_SSE2)
- // At least a multiple of 16, with SSE2.
- static_assert(PaddedInputDimensions % SimdWidth == 0);
- constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth;
- const __m128i Zeros = _mm_setzero_si128();
- const auto inputVector = reinterpret_cast<const __m128i*>(input);
-
-#elif defined(USE_MMX)
- static_assert(InputDimensions % SimdWidth == 0);
- constexpr IndexType NumChunks = InputDimensions / SimdWidth;
- const __m64 Zeros = _mm_setzero_si64();
- const auto inputVector = reinterpret_cast<const __m64*>(input);
-
-#elif defined(USE_NEON)
- static_assert(PaddedInputDimensions % SimdWidth == 0);
- constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth;
- const auto inputVector = reinterpret_cast<const int8x8_t*>(input);
-#endif
-
- for (IndexType i = 0; i < OutputDimensions; ++i) {
- const IndexType offset = i * PaddedInputDimensions;
-
-#if defined(USE_SSE2)
- __m128i sumLo = _mm_cvtsi32_si128(biases[i]);
- __m128i sumHi = Zeros;
- const auto row = reinterpret_cast<const __m128i*>(&weights[offset]);
- for (IndexType j = 0; j < NumChunks; ++j) {
- __m128i row_j = _mm_load_si128(&row[j]);
- __m128i input_j = _mm_load_si128(&inputVector[j]);
- __m128i extendedRowLo = _mm_srai_epi16(_mm_unpacklo_epi8(row_j, row_j), 8);
- __m128i extendedRowHi = _mm_srai_epi16(_mm_unpackhi_epi8(row_j, row_j), 8);
- __m128i extendedInputLo = _mm_unpacklo_epi8(input_j, Zeros);
- __m128i extendedInputHi = _mm_unpackhi_epi8(input_j, Zeros);
- __m128i productLo = _mm_madd_epi16(extendedRowLo, extendedInputLo);
- __m128i productHi = _mm_madd_epi16(extendedRowHi, extendedInputHi);
- sumLo = _mm_add_epi32(sumLo, productLo);
- sumHi = _mm_add_epi32(sumHi, productHi);
- }
- __m128i sum = _mm_add_epi32(sumLo, sumHi);
- __m128i sumHigh_64 = _mm_shuffle_epi32(sum, _MM_SHUFFLE(1, 0, 3, 2));
- sum = _mm_add_epi32(sum, sumHigh_64);
- __m128i sum_second_32 = _mm_shufflelo_epi16(sum, _MM_SHUFFLE(1, 0, 3, 2));
- sum = _mm_add_epi32(sum, sum_second_32);
- output[i] = _mm_cvtsi128_si32(sum);
-
-#elif defined(USE_MMX)
- __m64 sumLo = _mm_cvtsi32_si64(biases[i]);
- __m64 sumHi = Zeros;
- const auto row = reinterpret_cast<const __m64*>(&weights[offset]);
- for (IndexType j = 0; j < NumChunks; ++j) {
- __m64 row_j = row[j];
- __m64 input_j = inputVector[j];
- __m64 extendedRowLo = _mm_srai_pi16(_mm_unpacklo_pi8(row_j, row_j), 8);
- __m64 extendedRowHi = _mm_srai_pi16(_mm_unpackhi_pi8(row_j, row_j), 8);
- __m64 extendedInputLo = _mm_unpacklo_pi8(input_j, Zeros);
- __m64 extendedInputHi = _mm_unpackhi_pi8(input_j, Zeros);
- __m64 productLo = _mm_madd_pi16(extendedRowLo, extendedInputLo);
- __m64 productHi = _mm_madd_pi16(extendedRowHi, extendedInputHi);
- sumLo = _mm_add_pi32(sumLo, productLo);
- sumHi = _mm_add_pi32(sumHi, productHi);
- }
- __m64 sum = _mm_add_pi32(sumLo, sumHi);
- sum = _mm_add_pi32(sum, _mm_unpackhi_pi32(sum, sum));
- output[i] = _mm_cvtsi64_si32(sum);
-
-#elif defined(USE_NEON)
- int32x4_t sum = {biases[i]};
- const auto row = reinterpret_cast<const int8x8_t*>(&weights[offset]);
- for (IndexType j = 0; j < NumChunks; ++j) {
- int16x8_t product = vmull_s8(inputVector[j * 2], row[j * 2]);
- product = vmlal_s8(product, inputVector[j * 2 + 1], row[j * 2 + 1]);
- sum = vpadalq_s16(sum, product);
- }
- output[i] = sum[0] + sum[1] + sum[2] + sum[3];
-
-#else
- OutputType sum = biases[i];
- for (IndexType j = 0; j < InputDimensions; ++j) {
- sum += weights[offset + j] * input[j];