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* <code>VPDPBUSD</code> - Multiplies the individual bytes (8-bit) of the first source operand by the corresponding bytes (8-bit) of the second source operand, producing intermediate word (16-bit) results which are summed and accumulated in the double word (32-bit) of the destination operand.
 
* <code>VPDPBUSD</code> - Multiplies the individual bytes (8-bit) of the first source operand by the corresponding bytes (8-bit) of the second source operand, producing intermediate word (16-bit) results which are summed and accumulated in the double word (32-bit) of the destination operand.
* <code>VPDPBUSDS</code> - Same as above except on intermediate sum overflow which saturates to 0x7FFF_FFFF/0x8000_0000 for positive/negative numbers.
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** <code>VPDPBUSDS</code> - Same as above except on intermediate sum overflow which saturates to 0x7FFF_FFFF/0x8000_0000 for positive/negative numbers.
 
* <code>VPDPWSSD</code> - Multiplies the individual words (16-bit) of the first source operand by the corresponding word (16-bit) of the second source operand, producing intermediate word results which are summed and accumulated in the double word (32-bit) of the destination operand.
 
* <code>VPDPWSSD</code> - Multiplies the individual words (16-bit) of the first source operand by the corresponding word (16-bit) of the second source operand, producing intermediate word results which are summed and accumulated in the double word (32-bit) of the destination operand.
* <code>VPDPWSSDS</code> - Same as above except on intermediate sum overflow which saturates to 0x7FFF_FFFF/0x8000_0000 for positive/negative numbers.
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** <code>VPDPWSSDS</code> - Same as above except on intermediate sum overflow which saturates to 0x7FFF_FFFF/0x8000_0000 for positive/negative numbers.
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== Motivation ==
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The major motivation behind the AVX512 VNNI extension is the observation that many tight [[convolutional neural network]] loops require the repeated multiplication of two 16-bit values or two 8-bit values and accumulate the result to a 32-bit accumulator. Using the {{x86|AVX512F|foundation AVX-512}}, for 16-bit, this is possible using two instructions - <code>VPMADDWD</code> which is used to multiply two 16-bit pairs and add them together followed a <code>VPADDD</code> which adds the accumulate value.
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:[[File:vnni-vpdpwssd.svg|600px]]
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Likewise, for 8-bit values, three instructions are needed - <code>VPMADDUBSW</code> which is used to multiply two 8-bit pairs and add them together, followed by a <code>VPMADDWD</code> with the value <code>1</code> in order to simply up-convert the 16-bit values to  32-bit values, followed by the <code>VPADDD</code> instruction which adds the result to an accumulator.
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:[[File:vnni-vpdpbusd.svg|600px]]
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To address those two common operations, two new instructions were added (as well as two saturated versions): <code>VPDPBUSD</code> fuses <code>VPMADDUBSW</code>, <code>VPMADDWD</code>, and <code>VPADDD</code> and <code>VPDPWSSD</code> fuses <code>VPMADDWD</code> and <code>VPADDD</code>.
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:[[File:vnni-vpdpbusd-i.svg|400px]] [[File:vnni-vpdpwssd-i.svg|400px]]
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== Bibliography ==
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* {{hcbib|30}}
  
 
[[category:x86]]
 
[[category:x86]]

Revision as of 00:28, 7 November 2018

AVX-512 Vector Neural Network Instructions (AVX512 VNNI) is an x86 extension, part of the AVX-512, designed to accelerate convolutional neural network-based algorithms.

Overview

The AVX512 VNNI x86 extension extends AVX-512 Foundation by introducing four new instructions for accelerating inner convolutional neural network loops.

  • VPDPBUSD - Multiplies the individual bytes (8-bit) of the first source operand by the corresponding bytes (8-bit) of the second source operand, producing intermediate word (16-bit) results which are summed and accumulated in the double word (32-bit) of the destination operand.
    • VPDPBUSDS - Same as above except on intermediate sum overflow which saturates to 0x7FFF_FFFF/0x8000_0000 for positive/negative numbers.
  • VPDPWSSD - Multiplies the individual words (16-bit) of the first source operand by the corresponding word (16-bit) of the second source operand, producing intermediate word results which are summed and accumulated in the double word (32-bit) of the destination operand.
    • VPDPWSSDS - Same as above except on intermediate sum overflow which saturates to 0x7FFF_FFFF/0x8000_0000 for positive/negative numbers.

Motivation

The major motivation behind the AVX512 VNNI extension is the observation that many tight convolutional neural network loops require the repeated multiplication of two 16-bit values or two 8-bit values and accumulate the result to a 32-bit accumulator. Using the foundation AVX-512, for 16-bit, this is possible using two instructions - VPMADDWD which is used to multiply two 16-bit pairs and add them together followed a VPADDD which adds the accumulate value.

vnni-vpdpwssd.svg

Likewise, for 8-bit values, three instructions are needed - VPMADDUBSW which is used to multiply two 8-bit pairs and add them together, followed by a VPMADDWD with the value 1 in order to simply up-convert the 16-bit values to 32-bit values, followed by the VPADDD instruction which adds the result to an accumulator.

vnni-vpdpbusd.svg

To address those two common operations, two new instructions were added (as well as two saturated versions): VPDPBUSD fuses VPMADDUBSW, VPMADDWD, and VPADDD and VPDPWSSD fuses VPMADDWD and VPADDD.

vnni-vpdpbusd-i.svg vnni-vpdpwssd-i.svg

Bibliography