eIQ® Inference with DeepViewRT™

EIQ-INFERENCE-DEEPVIEWRT

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eIQ Inference with DeepViewRT™ is a platform-optimized, runtime inference engine that scales across a wide range of NXP devices and neural network compute engines. Provided free-of-charge, this inference engine enables compact code size for resource-constrained devices including the i.MX RT crossover MCUs (Arm® Cortex®-M cores), i.MX applications processors (Cortex-A and Cortex-M cores, dedicated neural processing units (NPU) and GPUs​).

This inference engine is delivered via NXP standard Yocto BSP release for Linux® OS-based development, and MCUXpresso SDK release for embedded RTOS-enabled MCU development.

Diagram

eIQ Inference with DeepViewRT Block Diagram

eIQ Inference with DeepViewRT Block Diagram

Features

  • Platform-optimized for running machine learning models on i.MX RT crossover MCUs and i.MX 8M family of applications processors
  • Provides the ability to run inferencing on Arm® Cortex®-M and A cores, GPUs and NPUs
  • Delivered as middleware in NXP MCUXpresso SDK and Yocto BSP release for Linux OS-based development
  • Enabled by exclusive partnership with Au-Zone Technologies

Supported Devices

  • i.MX8M: i.MX 8M Family - Arm® Cortex®-A53, Cortex-M4, Audio, Voice, Video
  • i.MX8MMINI: i.MX 8M Mini - Arm® Cortex®-A53, Cortex-M4, Audio, Voice, Video
  • i.MX8MNANO: i.MX 8M Nano Family - Arm® Cortex®-A53, Cortex-M7
  • IMX8MPLUS: i.MX 8M Plus – Arm® Cortex®-A53, Machine Learning, Vision, Multimedia and Industrial IoT
  • i.MX-RT1064: i.MX RT1064: Crossover MCU with Arm® Cortex®-M7
  • i.MX-RT1170: i.MX RT1170: 1 GHz Crossover MCU with Arm® Cortex® Cores

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    MCUXpresso SDK Builder

    EXTERNALRev 1Jan 28, 2021 02:41:001 KBMCUXPRESSO-SDK-EIQ
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Y true 0 SSPEIQ-INFERENCE-DEEPVIEWRTen 2 Application Note Application Note t789 1 Fact Sheet Fact Sheet t523 1 en_US en_US en Application Note Application Note 1 1 1 Chinese This application note presents the process of building and deploying deep learning models for Smart Sensing Appliances. It also highlights how to validate and evaluate the performance of a model by running it through different inference engines on an Embedded Sensing Device. 1644318754124703028011zh SSP 4.9 MB None None documents None 1644318754124703028011 /docs/zh/application-note/AN13562.pdf 4943726 /docs/zh/application-note/AN13562.pdf AN13562 documents N N 2022-02-08 Building and Benchmarking Deep Learning Models for Smart Sensing Appliances on MCUs /docs/zh/application-note/AN13562.pdf /docs/zh/application-note/AN13562.pdf Application Note N 645036621402383989 2024-07-17 zh Apr 25, 2022 645036621402383989 Application Note Y N Building and Benchmarking Deep Learning Models for Smart Sensing Appliances on MCUs 2 English AN13562: This application note presents the process of building and deploying deep learning models for Smart Sensing Appliances. It also highlights how to validate and evaluate the performance of a model by running it through different inference engines on an Embedded Sensing Device. 1644318754124703028011 SSP 4.9 MB None None documents None 1644318754124703028011 /docs/en/application-note/AN13562.pdf 4943726 /docs/en/application-note/AN13562.pdf AN13562 documents N N 2022-02-08 Building and Benchmarking Deep Learning Models for Smart Sensing Appliances on MCUs /docs/en/application-note/AN13562.pdf /docs/en/application-note/AN13562.pdf Application Note N 645036621402383989 2024-07-17 pdf N en Sep 27, 2023 645036621402383989 Application Note Y N Building and Benchmarking Deep Learning Models for Smart Sensing Appliances on MCUs Fact Sheet Fact Sheet 1 2 4 Japanese Machine learning software for NXP i.MX and MCUs – libraries, example applications, inference engines, HALs 1562948465206707057398ja SSP 653.2 KB None None documents None 1562948465206707057398 /docs/ja/fact-sheet/EIQ-FS.pdf 653150 /docs/ja/fact-sheet/EIQ-FS.pdf EIQ-FS documents N N 2019-07-12 eIQ Software Fact Sheet /docs/ja/fact-sheet/EIQ-FS.pdf /docs/ja/fact-sheet/EIQ-FS.pdf Fact Sheet N 736675474163315314 2022-12-07 ja Jul 11, 2023 736675474163315314 Fact Sheet Y N eIQ 機械学習ソフトウェア開発環境 4 English Machine learning software for NXP i.MX and MCUs – libraries, example applications, inference engines, HALs 1562948465206707057398 SSP 653.2 KB None None documents None 1562948465206707057398 /docs/en/fact-sheet/EIQ-FS.pdf 653150 /docs/en/fact-sheet/EIQ-FS.pdf EIQ-FS documents N N 2019-07-12 eIQ Software Fact Sheet /docs/en/fact-sheet/EIQ-FS.pdf /docs/en/fact-sheet/EIQ-FS.pdf Fact Sheet N 736675474163315314 2022-12-07 pdf N en Jan 21, 2022 736675474163315314 Fact Sheet Y N eIQ Software Fact Sheet false 0 EIQ-INFERENCE-DEEPVIEWRT downloads en true 1 Y SSP Y Y Application Note 1 /docs/en/application-note/AN13562.pdf 2022-02-08 1644318754124703028011 SSP 1 Sep 27, 2023 Application Note AN13562: This application note presents the process of building and deploying deep learning models for Smart Sensing Appliances. It also highlights how to validate and evaluate the performance of a model by running it through different inference engines on an Embedded Sensing Device. None /docs/en/application-note/AN13562.pdf English documents 4943726 None 645036621402383989 2024-07-17 N /docs/en/application-note/AN13562.pdf Building and Benchmarking Deep Learning Models for Smart Sensing Appliances on MCUs /docs/en/application-note/AN13562.pdf documents 645036621402383989 Application Note N en None Y pdf 2 N N Building and Benchmarking Deep Learning Models for Smart Sensing Appliances on MCUs 4.9 MB AN13562 N 1644318754124703028011 Fact Sheet 1 /docs/en/fact-sheet/EIQ-FS.pdf 2019-07-12 1562948465206707057398 SSP 2 Jan 21, 2022 Fact Sheet Machine learning software for NXP i.MX and MCUs – libraries, example applications, inference engines, HALs None /docs/en/fact-sheet/EIQ-FS.pdf English documents 653150 None 736675474163315314 2022-12-07 N /docs/en/fact-sheet/EIQ-FS.pdf eIQ Software Fact Sheet /docs/en/fact-sheet/EIQ-FS.pdf documents 736675474163315314 Fact Sheet N en None Y pdf 4 N N eIQ Software Fact Sheet 653.2 KB EIQ-FS N 1562948465206707057398 true Y Softwares

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