Provide Feedback

Try Renesas RZ/V2MA SMARC SOM : Introducing the New LiveBench Lab

We are excited to announce the launch of our latest LiveBench Lab, highlighting the remarkable capabilities of the RZ/V2MA SMARC SOM. This advanced development platform is designed to enhance the creation of AI-driven embedded applications, machine vision systems, and sophisticated image processing solutions.

Why the RZ/V2MA SMARC SOM?

The RZ/V2MA features a highly power-efficient AI accelerator (DRP-AI) and an OpenCV accelerator, offering an exceptional balance between performance and power consumption. It includes dual Cortex-A53 CPU cores and a suite of high-speed interfaces, making it an ideal choice for various applications. Here’s what makes the RZ/V2MA SMARC SOM stand out:

  • AI Acceleration: The DRP-AI provides 1.0 TOPS/W class power performance, combined with an OpenCV accelerator for seamless image processing without the need for API modifications.

  • High-Speed Interfaces: With 1× Gigabit Ethernet, 1× USB3.1 Gen1, 1× PCIe® Gen 2 (2 lanes), 2× SDIO 3.0, and 1× eMMC™ 4.5.1, the RZ/V2MA offers versatile connectivity options to meet various project requirements.

  • Video and Graphics Processing: Support for H.265/H.264 multi codec with encoding capabilities up to 2160p for H.265 and up to 1080p for H.264 ensures superior video quality.

  • Memory and CPU: Dual Cortex-A53 CPUs running at up to 996 MHz and a 32-bit LPDDR4-3200 memory interface provide robust processing power and memory bandwidth.

  • Compact Package: The FCBGA package with a 15×15 mm size and 0.5-mm pitch is perfect for space-constrained applications.
 

Explore the LiveBench Lab Capabilities

 

Our new LiveBench Lab is designed to help you explore the full potential of the RZ/V2MA SMARC SOM

Here’s what you can do with it:

  • Run Machine Vision Algorithms: Execute machine vision algorithms on three different types of input feeds (demo video, user-uploaded video, and live webcam feed) to optimize performance.

  • Compare Performance: Effortlessly compare the performance of various machine learning models for object detection, pose estimation, and more.

  • Monitor System Parameters: Gain deep insights into electrical and thermal variations during algorithm execution for system stability evaluation. Monitor voltage, current, and power fluctuations, as well as temperature variations using a thermal camera.

  • Power Cycling: Observe the variation of initial transient current, voltage, and power in real time by applying a power cycle operation on the board.

  • Benchmarking: Benchmark the board’s performance using standard Python scripts and a CLI to ensure it meets your project requirements.
 
 

Get Started Today!

The new LiveBench Lab for the RZ/V2MA SMARC SOM is now live and ready for you to explore. Whether you’re developing AI-driven embedded applications or advanced image processing solutions, this lab provides all the tools you need to evaluate and optimize your projects.

Check out the lab now and see for yourself how the RZ/V2MA SMARC SOM can transform your next project
Table of Contents
Share the Post:
Prabrit Bandyopadhyay
Prabrit Bandyopadhyay is a seasoned professional with expertise in Electrical and Electronics Engineering, Machine Learning, and Embedded System Design. Holding an M.Tech in Mechatronics from NITTTR, Kolkata, he boasts over a decade of experience in both academia and industry. Currently serving as a Hardware Research and Development Engineer at Tenxer Labs India Pvt. Ltd., Prabrit specializes in Embedded Systems, Machine Learning, Edge AI, and Power Electronics, reflecting his diverse skill set and multidisciplinary proficiency.
Related Reads
Introducing the New LiveBench Interface: A Streamlined Experience for IC Exploration and Evaluation
Sep 13, 2024
Read More
Digital Transformation in Field Applications Engineering – Insights from Our Latest Roundtable
Sep 11, 2024
Read More
Announcing Our New Lab : RL78/G23 Lighting Communication Master Evaluation Board to Accelerate Your Lighting Control Designs with DALI
Sep 9, 2024
Read More

Was the content on this page helpful?