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Plumerai Video Intelligence platform support

Supported platforms

The Plumerai Video Intelligence library is optimized for fast, memory-efficient inference across a wide range of processors, from embedded devices to high-performance systems. It leverages SIMD and AI accelerators where available to maximize efficiency and is extensively tested for reliability and compatibility across the following platforms:

  • Arm Cortex-A with/without Neon:
    • Armv7 and aarch64/ARM64.
    • Cortex-A5, A7, A9, A15, A35, A53, A72, etc.
    • NXP i.MX, QualComm Snapdragon, Renesas RZ, TI Sitara.
    • Raspberry Pi 3/4/5 and similar devices.
  • Arm Cortex-M with/without Helium:
    • Arm Cortex-M33, M4, M7, M55, M85.
  • Neural Processing Units (NPUs) and AI accelerators:
    • Ambarella CV series 'Vector Processor' VP (e.g. CV28).
    • Anyka NNE NPU.
    • Arm Ethos-U.
    • Realtek Ameba Pro2 'Intelligent Engine' NPU.
    • SigmaStar IPU.
  • x86 and x86_64 with SSE2, SSE4, AVX, AVX2, AVX512:
    • Intel Celeron, i3, i5, i7, Xeon, etc.
    • AMD Opteron, Ryzen, Epyc, etc.
    • Desktops, laptops, cloud.
  • Microcontrollers (MCUs):
    • Arm Cortex-M (see above).
    • Espressif ESP32-S3 with Tensilica Xtensa LX7.
    • Synopsys ARC EM4.
    • RISC-V.

The Plumerai Video Intelligence library can run on:

  • Bare-metal systems.
  • RTOS-based systems.
  • Full operating systems: Linux, Windows, macOS.

Additional features:

  • Supports static memory allocation, making it compatible with systems that lack dynamic memory allocation.
  • Can operate without requiring the C++ standard library, enabling use in highly constrained environments.

Expected latency

Below is a table with expected latency numbers for some example Arm CPUs and input resolutions1:

OS CPU Threads Latency @ 800x600x3 Latency @ 1600x1200x3
32-bit Cortex-A53 @ 1.2GHz2 1 42 ms 44 ms
64-bit Cortex-A53 @ 1.2GHz2 1 42 ms 44 ms
64-bit Cortex-A72 @ 1.5GHz3 1 20 ms 21 ms

  1. Note that any input resolution is supported. 

  2. BCM2837, Raspberry Pi 3B Rev 1.2 

  3. BCM2711, Raspberry Pi 4B Rev 1.5