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Results 61 - 70 of 106 for multiplication (0.19 sec)
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src/math/big/float.go
z.form = zero z.neg = false return } // len(z.mant) > 0 z.setExpAndRound(ex+int64(len(z.mant))*_W-fnorm(z.mant), 0) } // z = x * y, ignoring signs of x and y for the multiplication // but using the sign of z for rounding the result. // x and y must have a non-empty mantissa and valid exponent. func (z *Float) umul(x, y *Float) { if debugFloat { validateBinaryOperands(x, y) }
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Thu Jun 06 15:46:54 UTC 2024 - 44.5K bytes - Viewed (0) -
src/crypto/aes/gcm_amd64.s
//go:build !purego // This is an optimized implementation of AES-GCM using AES-NI and CLMUL-NI // The implementation uses some optimization as described in: // [1] Gueron, S., Kounavis, M.E.: IntelĀ® Carry-Less Multiplication // Instruction and its Usage for Computing the GCM Mode rev. 2.02 // [2] Gueron, S., Krasnov, V.: Speeding up Counter Mode in Software and // Hardware #include "textflag.h" #define B0 X0
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Mon Mar 04 17:29:44 UTC 2024 - 23.4K bytes - Viewed (0) -
tensorflow/c/eager/c_api_unified_experimental_test.cc
TF_DeleteAbstractOp(add_op); // Extract the resulting tensor. add_output2 = TF_OutputListGet(add_outputs, 0); TF_DeleteOutputList(add_outputs); } // 3rd Output will be Matrix Multiplication of add_output1 and add_output2 TF_AbstractTensor* mm_output; { // Build an abstract operation, inputs and output. auto* mm_op = TF_NewAbstractOp(graph_ctx); TF_AbstractOpSetOpType(mm_op, "MatMul", s);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 19 21:44:52 UTC 2023 - 39.1K bytes - Viewed (0) -
src/image/png/reader.go
rgba64 *image.RGBA64 nrgba64 *image.NRGBA64 img image.Image ) width, height := d.width, d.height if d.interlace == itAdam7 && !allocateOnly { p := interlacing[pass] // Add the multiplication factor and subtract one, effectively rounding up. width = (width - p.xOffset + p.xFactor - 1) / p.xFactor height = (height - p.yOffset + p.yFactor - 1) / p.yFactor
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Thu Oct 19 12:02:45 UTC 2023 - 26K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/python/integration_test/quantize_model_test.py
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 14 06:31:57 UTC 2024 - 51.4K bytes - Viewed (0) -
tensorflow/cc/gradients/math_grad_test.cc
const bool t_x, const bool t_y, std::function<Output(Output, Output)> mul_fn) { TF_ASSERT_OK(root_.status()); // Generate random (but compatible) shapes for matrix multiplication. std::vector<TensorShape> shapes; RandMatMulShapes(is_x_batch, is_y_batch, t_x, t_y, &shapes); TensorShape x_shape = shapes[0]; TensorShape y_shape = shapes[1]; TensorShape z_shape = shapes[2];
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Aug 25 18:20:20 UTC 2023 - 36K bytes - Viewed (0) -
src/math/big/int.go
if k > n { return z.SetInt64(0) } // reduce the number of multiplications by reducing k if k > n-k { k = n - k // C(n, k) == C(n, n-k) } // C(n, k) == n * (n-1) * ... * (n-k+1) / k * (k-1) * ... * 1 // == n * (n-1) * ... * (n-k+1) / 1 * (1+1) * ... * k // // Using the multiplicative formula produces smaller values // at each step, requiring fewer allocations and computations: //
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Thu Mar 14 17:02:38 UTC 2024 - 33.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/python/integration_test/quantize_model_test_base.py
return self.has_bias() and self.bias_size != self.filters.shape[-1] @def_function.function def matmul(self, input_tensor: core.Tensor) -> Mapping[str, core.Tensor]: """Performs a matrix multiplication. Depending on self.has_bias and self.activation_fn, it may add a bias term or go through the activaction function. Args:
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Mar 21 08:51:46 UTC 2024 - 51.2K bytes - Viewed (0) -
src/cmd/compile/internal/ssa/_gen/S390X.rules
(RISBGZ x {r}) && r == s390x.NewRotateParams(32, 63, 0) => (MOVWZreg x) // Use sign/zero extend instead of ANDW. (ANDWconst [0x00ff] x) => (MOVBZreg x) (ANDWconst [0xffff] x) => (MOVHZreg x) // Strength reduce multiplication to the sum (or difference) of two powers of two. // // Examples: // 5x -> 4x + 1x // 10x -> 8x + 2x // 120x -> 128x - 8x // -120x -> 8x - 128x //
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Thu Oct 12 18:09:26 UTC 2023 - 74.3K bytes - Viewed (0) -
src/cmd/compile/internal/ssa/_gen/RISCV64.rules
// Merge negation into fused multiply-add and multiply-subtract. // // Key: // // [+ -](x * y [+ -] z). // _ N A S // D U // D B // // Note: multiplication commutativity handled by rule generator. (F(MADD|NMADD|MSUB|NMSUB)S neg:(FNEGS x) y z) && neg.Uses == 1 => (F(NMSUB|MSUB|NMADD|MADD)S x y z)
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Thu Mar 07 14:57:07 UTC 2024 - 40.3K bytes - Viewed (0)