Intel® Integrated Performance Primitives (Intel® IPP) & Their Applications

Speed performance for imaging, vision, signal, security, and storage applications.

  • Multicore, multiple operating system, and multiplatform ready, computationally intensive and highly optimized functions.
  • Plug in and use APIs to quickly improve application performance.
  • Reduced cost and time-to-market (TTM) on software development and maintenance.
  • Take advantage of Priority Support―connect privately with Intel engineers for technical questions.

Domains in Intel® Integrated Performance Primitives (Intel® IPP) and their applications

Image Processing

From the everyday snapping of camera phones and image enhancement via playful apps to advanced biometric sensing, factory machine vision, and driverless automobiles-image recognition, processing plays a very important role. Intel® IPP takes visual information and converts it into usable data for further analysis and decision-making. As the sheer volume of imaging information captured by vision systems continues to rise, image processing steps in to convert image arrays into manageable units.

Image processing applications where Intel IPP is used:

  • Healthcare (medical imaging)
  • Computer vision
  • Visual search for e-commerce
  • Digital surveillance
  • Biometric identification
  • Factory machine vision
  • ADAS (autonomous driving)
  • Printing and printers
  • Image recognition
  • Remote equipment operation
  • Gesture recognition
  • Illegal image recognition
  • Optical correction

Signal Processing

Signal processing enables the generation, transformation, and interpretation of information. It pulls meaning from broad sources of data all around us. It is at the heart of the modern world, helping modern communications including voice recognition, biotechnology, wearable technology, hearing aids, speech synthesis, and many more. Intel IPP optimized commonly used signal processing functions―like DFT, FFT, convolution, filtering, and statistics for a wide variety of Intel® architectures.

Signal processing applications where Intel IPP is used:

  • Telecommunications
  • Energy
  • Ultra sound machines
  • Medical scanning
  • Recording, enhancement, and playback of audio and non-audio signals
  • Echo cancellation: filtering, equalization, and emphasis
  • Simulation of environment or acoustics
  • Games involving sophisticated audio content or effect
  • Voice controlled personal assistants

Data Compression

Data storage and management are high priorities with growth in connecting cloud and data centers to edge devices. Data compression is the art of reducing the number of bits needed to store or transmit data. Commonly used compression standards like Lempel-Ziv-Storer-Szymanski (LZSS), LZ77 (Zlib), Lempel-Zib-Oberhumer (LZO), and bzip2 are highly optimized in Intel IPP. With the plug-and-use nature of Intel IPP functions, seeing a significant performance gain on these applications is easy.

  • Internet portal data center
  • Data storage centers
  • Databases
  • Enterprise data management

Cryptography

Cybersecurity is growing in importance in areas like security analysis, threat intelligence, mobile security, cloud security, and IoT security. Security also plays a crucial role in autonomous and self-driving cars for protection against cyberattacks and intrusion. Intel IPP has a multitude of functions for data integrity and authentication hash (SHA, MD5, SM3), public key cryptography (RSA, ECC, HMAC, CMAC), and secure data transfer, such as symmetric algorithms, Advanced Encryption Standard (AES), Triple DES (TDES), SMS4, and steam ciphers.

Security applications where Intel IPP is used:

  • Telecommunications
  • Transaction security and cybersecurity
  • Smart card and wallet interfaces
  • ID verification
  • Copy protection
  • Electronic signature
  • ADAS

For more details on the different domains and the functions available in each domain, see the release notes and documentation.

Performance numbers and benchmarks for the different domains are available in the Benchmarks section.