Renesas announced the RZ/V2N on March 11, 2025, as a mid-range addition to its RZ/V embedded-AI MPU family. It combines an AI accelerator rated for up to 15 TOPS with two camera interfaces in a 15 × 15 mm package, targeting products such as AI cameras, driver-monitoring systems and mobile robots. Renesas says mass production began in March 2025; the company lists an active RZ/V2N evaluation kit.
What is the Renesas RZ/V2N?
The RZ/V2N is a vision-focused microprocessor unit (MPU) for embedded devices that analyze camera images locally. Its processing mix includes four Arm Cortex-A55 application cores, a Cortex-M33 microcontroller core, a Mali-C55 image signal processor (ISP) and Renesas’ DRP-AI3 accelerator. The combination is intended to handle application software, real-time control, image processing and AI inference within one system.
Renesas positions it between the lower-performance RZ/V2L and higher-performance RZ/V2H. The V2N is aimed at mid-range endpoint systems where camera processing and AI matter, but where a top-end accelerator may not be necessary.
How much AI performance does it offer?
Renesas rates the DRP-AI3 in the RZ/V2N at up to 15 TOPS of AI inference and 10 TOPS/W. These are manufacturer-stated peak performance and efficiency figures; the supplied specification does not identify a workload, precision, measurement setup or operating conditions for the efficiency rating. TOPS is a throughput rating, not a promise that a particular application will run at that speed. Actual results depend on the model, its optimization, image pipeline and system configuration.
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- Dual-Brain Hybrid Power: Combines the Qualcomm Dragonwing QRB2210 MPU (Quad-core Arm Cortex-A53 @ 2.0 GHz CPU, Adreno GPU, AI acceleration) and the real-time, low-power STM32U585 MCU for advanced applications like object recognition, voice commands, and motion detection.
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- Advanced Features: Equipped with 2 GB LPDDR4 RAM, 16 GB eMMC built-in storage, ideal to develop in PC-connected mode, running the OS, Python scripts, and basic network services (SSH) without a demanding GUI or heavy multitasking; great for lightweight AI and memory-optimized TinyML applications, needing local storage for basic OS and core libraries. Dual-band Wi-Fi 5 (2.4/5 GHz), Bluetooth 5.1, and high-speed headers for vision, audio, and display peripherals.
- Seamless Expansion & Connectivity: Features the classic UNO form factor for shields compatibility, an 8x13 LED matrix, and a Qwiic connector for easy expansion with Modulino nodes; power and connect via the USB-C connector.
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Renesas says its pruning technology reduces unnecessary computation during inference. Its software environment also uses hardware-software co-optimization and includes more than 50 AI use cases, according to the company’s 2025 announcement. The white paper describes support for PyTorch and TensorFlow workflows and an AI-MAC paired with dynamically reconfigurable processing.
What makes it suitable for two-camera applications?
The RZ/V2N has two four-lane MIPI CSI-2 camera interfaces and a Mali-C55 ISP. Two camera feeds can support stereo vision or simultaneous views from different angles. Renesas identifies motion analysis, fall detection, parking-lot vehicle counting and license-plate recognition as examples of applications that can benefit from this configuration.
Its target workloads extend beyond those examples to AI cameras, traffic and congestion analysis, industrial visual inspection, driver monitoring, mobile robots, retail, logistics, surveillance and vision-AI gateways. Two interfaces make a dual-camera design possible; they do not by themselves establish supported sensor combinations, frame rates for every mode or application-level accuracy. Those depend on the camera modules, software and system design.
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RZ/V2N compared with RZ/V2L and RZ/V2H
Renesas’ current product catalog, accessed in 2026, places the portfolio between 0.5 TOPS and up to 80 TOPS. The table distinguishes published portfolio-level AI ratings from details not established in the cited announcement, product catalog or white paper.
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| Specification | RZ/V2L | RZ/V2N | RZ/V2H |
|---|---|---|---|
| AI performance | 0.5 TOPS (Renesas current product catalog, accessed 2026) | Up to 15 TOPS; 10 TOPS/W rated efficiency (Renesas, 2025) | Up to 80 TOPS (Renesas current product catalog, accessed 2026) |
| Camera count / interfaces | Not stated in the cited material | Two four-lane MIPI CSI-2 interfaces | Not stated in the cited material |
| CPU and memory interfaces | Not stated in the cited material | Four 1.8 GHz Cortex-A55 cores, one 200 MHz Cortex-M33; 32-bit LPDDR4/4X-3200 interface | Not stated in the cited material |
| Package and mounting area | Not stated in the cited material | 840-pin, 15 × 15 mm FCBGA; Renesas says its mounting area is 38% smaller than RZ/V2H | Not stated in the cited material; used as the RZ/V2N mounting-area comparison |
| Video capability | Not stated in the cited material | H.264/H.265 encode and decode at 4K/30 fps | Not stated in the cited material |
| Power efficiency | Not stated in the cited material | 10 TOPS/W, a Renesas rating; test conditions are not stated | Not stated in the cited material |
| Development cost | Not stated in the cited material | Not stated in the cited material | Not stated in the cited material |
These figures support a broad positioning comparison, not a complete performance or cost ranking. The cited sources do not provide matching camera, CPU, memory, video, efficiency or price details for all three processors, so those differences cannot be quantified here.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What are the RZ/V2N’s other hardware specifications?
Beyond its CPU, ISP and AI accelerator, the published specification lists Mali-G31 3D graphics, two Gigabit Ethernet ports, one USB 3.2 Gen 2 port, one USB 2.0 port, six CAN-FD channels and PCIe Gen3. Memory is LPDDR4/4X-3200 on a 32-bit interface. Video encoding and decoding are specified for H.264 and H.265 at 4K/30 fps.
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The 840-pin FCBGA package measures 15 × 15 mm. Renesas says the RZ/V2N’s mounting area is 38% smaller than the RZ/V2H’s; that comparison concerns mounting area, not a stated 38% reduction in package dimensions or total system size.
How can developers evaluate the RZ/V2N?
Renesas lists an active product called the RZ/V2N Quad-core Vision AI MPU Evaluation Kit with an “Order Now” path. Availability, price and shipping depend on the seller and region, so check Renesas or its authorized distributors for current ordering details.
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Who should consider the RZ/V2N?
The RZ/V2N is a candidate for teams building embedded vision products that need local AI inference and may use two camera inputs, such as surveillance devices, driver-monitoring systems, inspection equipment or mobile robots. Its portfolio position suggests a middle option between the lower-rated V2L and higher-rated V2H, but the published TOPS figures alone cannot establish which MPU is the best fit. Compare the target model’s measured performance, camera and video requirements, memory needs, thermal and power budget, software support and total system cost before selecting a processor.
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