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2. AVX-512 - Wikipedia

   en.wikipedia.org/wiki/AVX-512

   AVX-512 are 512-bit extensions to the 256-bit Advanced Vector Extensions SIMD instructions for x86 instruction set architecture (ISA) proposed by Intel in July 2013, and first implemented in the 2016 Intel Xeon Phi x200 (Knights Landing), [1] and then later in a number of AMD and other Intel CPUs (see list below).

3. AES instruction set - Wikipedia

   en.wikipedia.org/wiki/AES_instruction_set

   [52] [53] [54] The AVX-512 Galois Field New Instructions (GFNI) allows implementing these S-boxes in a more direct way. [55] New cryptographic algorithms have been constructed to specifically use parts of the AES algorithm, so that the AES instruction set can be used for speedups.

4. Advanced Vector Extensions - Wikipedia

   en.wikipedia.org/wiki/Advanced_Vector_Extensions

   The first and "early" version of AVX10, notated AVX10.1, will not introduce any instructions or encoding features beyond what is already in AVX-512 (specifically, in Intel Sapphire Rapids: AVX-512F, CD, VL, DQ, BW, IFMA, VBMI, VBMI2, BITALG, VNNI, GFNI, VPOPCNTDQ, VPCLMULQDQ, VAES, BF16, FP16). The second and "fully-featured" version, AVX10.2 ...

5. Streaming SIMD Extensions - Wikipedia

   en.wikipedia.org/wiki/Streaming_SIMD_Extensions

   Advanced Vector Extensions (AVX), Gesher New Instructions (GNI), is an advanced version of SSE announced by Intel featuring a widened data path from 128 bits to 256 bits and 3-operand instructions (up from 2). Intel released processors in early 2011 with AVX support. [7] AVX2 is an expansion of the AVX instruction set.

6. Intel SHA extensions - Wikipedia

   en.wikipedia.org/wiki/Intel_SHA_extensions

   The newer SHA-512 instruction set comprises AVX-based versions of the original SHA instruction set marked with a V prefix and these three new AVX-based instructions for SHA-512: VSHA512RNDS2, VSHA512MSG1, VSHA512MSG2

7. DL Boost - Wikipedia

   en.wikipedia.org/wiki/DL_Boost

   DL Boost consists of two sets of features: AVX-512 VNNI, 4VNNIW, or AVX-VNNI: fast multiply-accumulation mainly for convolutional neural networks.; AVX-512 BF16: lower-precision bfloat16 floating-point numbers for generally faster computation.