Facebook to Open QNNPACK's Source Code

The technology, designed for AI on mobile devices, is used in Facebook applications for image processing
30 October 2018   633

Facebook has released the open source code for the QNNPACK AI library (Quantized Neural Network PACKage). It is designed for AI on mobile devices. The technology is used in Facebook applications for image processing. Since the computing power of mobile devices is lower than that of data processing servers, the developers used the latest advances in neural networks to keep the system performance at the proper level.

The library architecture is based on the convolutional neural network. Such a network is considered the most suitable for the recognition of visual images. To improve productivity, engineers applied the modified im2col memory matrix transformation and memory transformation technologies.

The im2col technology is a breakdown of the processed image into columns-vectors by the number of incoming channels. The creators of QNNPACK finalized this system by including a bypass buffer.

The bypass buffer contains pointers to rows of incoming pixels that should be involved in the calculation of outgoing. Using matrices, developers were able to reduce the buffer size relative to standard im2col implementations.

Facebook engineers refined the even-distributed convolution algorithm (depthwise convolution) by adding batch processing of 3 × 3 groups. For packet computing, general purpose registers (GPR) are used. The 3 × 3 convolution processing requires 18 registers (9 incoming and 9 for the filter), while the 32-bit ARM core architecture supports only 14. But since the filter remains unchanged during processing, the developers were able to reduce the resources needed for its storage to single register.

In the QNNPACK library, other advanced approaches to optimizing neural networks are implemented, for example, low-precision calculations.

The tool is published as part of the PyTorch 1.0 framework, released in early October 2018.

OpenAI to Create Fake News Creating Algorithm

On the basis of one or two phrases that set the theme, it is able to “write” a fairly plausible story
18 February 2019   6

The GPT-2 algorithm, created by OpenAI for working with language and texts, turned out to be a master in creating fake news. On the basis of one or two phrases that set the theme, it is able to “compose” a fairly plausible story. For example:

  • an article about scientists who have found a herd of unicorns in the Andes;
  • news about pop star Miley Cyrus caught on shoplifting;
  • artistic text about Legolas and Gimli attacking the orcs;
  • an essay on how waste recycling harms the economy, nature, and human health.

The developers did not publish the source code of the model entirely, fearing abuse by unscrupulous users. For fellow researchers, they posted on GitHub a simplified version of the algorithm and gave a link to the preprint of the scientific article. The overall results are published on the OpenAI blog.

GPT-2 is a general purpose algorithm. The developers taught it to answer questions, “understand” the logic of a text, a sentence, finish building phrases. In this case, the algorithm worked worse than the model of a specific purpose. Researchers suggest that the indicators can be improved by expanding the training datasets and choosing computers more efficiently.