Grinn ReneSOM-V2H is a tiny LGA SoM based on Renesas RZ/V2H processor for vision AI applications

Polish embedded systems company Grinn has recently introduced ReneSOM-V2H, a tiny vision AI SoM built around the Renesas RZ/V2H vision AI processor. Measuring just 42.6 x 37 mm, Grinn claims it is the world’s smallest module based on this specific Renesas MPU, and targets space-constrained Edge AI applications such as smart cameras, robotics, and industrial automation.

The RZ/V2H SoC features a heterogeneous architecture with 4x Cortex-A55 cores, 2x Cortex-R8 cores, and 1x Cortex-M33 core, along with a DRP-AI3 accelerator with up to 8 TOPS. It supports LPDDR4 memory and eMMC storage, along with various connectivity options, including PCIe Gen3 (4-lane), USB 3.2, USB 2.0, and Gigabit Ethernet. It also provides four MIPI-CSI camera inputs and a MIPI-DSI display output for vision applications.

Grinn ReneSOM-V2H specifications:

• SoC – Renesas RZ/V2H
  • CPU/MCU cores
    • 4x Arm Cortex-A55 cores up to 1.8 GHz
    • 2x Cortex-R8 real-time cores up to 800 MHz
    • Arm Cortex-M33 microcontroller core up to 200 MHz for system management
  • GPU – Arm Mali-G31 GPU
  • ISP – OpenCV Accelerator, ISP (optional Arm Mali-C55)
  • NPU
    • DRP-AI3 up to 8 TOPS (INT8) or 80 TOPS (Sparse)
    • DRP dynamically reconfigurable processor (STP4)
• System Memory – Up to 8GB supported by SoC (exact memory configuration on module not specified)
• Storage – eMMC flash (exact configuration not specified)
• LGA balls
  • Display Interface – MIPI DSI
  • Camera Inputs – 4x 4-lane MIPI CSI-2 interfaces
  • Networking – Gigabit Ethernet interface
  • USB
    • 1x USB 3.2 interface
    • 1x USB 2.0 interface
  • Expansion
    • PCIe Gen3 (4-lane)
    • 6x CAN FD, UART, I2C, SPI, ADC (should have support for this, but not confirmed by the company)
• Power Supply – 5V single supply
• Dimensions – 42.6 x 37 mm (LGA design with 260-pin SO-DIMM compatibility, whatever that means)
• Operating Temperature – −40°C to +85°C (industrial range)

Looking at the specifications, it seems that the module can handle up to four cameras with high-speed data throughput via PCIe Gen3 and USB 3.2. Which makes it very similar to a NVIDIA Jetson Orin Nano. Though the raw TOPS might seem lower (8 vs 20/40), the DRP-AI3 architecture is often more efficient for specific vision pipelines without requiring a fan.

The company does not mention anything about software. However, considering the module is based on a Renesas RZ/V2H SoC, it should have support for the full Renesas software ecosystem, including a Linux BSP based on Yocto, DRP-AI drivers, and the DRP-AI TVM backend for TensorFlow models. Additional support includes Flexible Software Package (FSP) components for Cortex-R8 and Cortex-M33 real-time cores, multi-OS or bare-metal development options, and Edge Impulse integration for DRP-AI-accelerated models.

Grinn suggests that using the ReneSOM-V2H can shorten development cycles by up to 12 months compared to a chip-down design, as it handles the complexities of high-speed routing for the RZ/V2H’s 1368-pin BGA package. The Grinn ReneSOM-V2H is currently sampling, and the company did not provide pricing information. More details can be found on the product page and the official announcement.

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