Retronix Technologies Inc. announced the launch of two cutting-edge AI edge computing platforms, developed in collaboration with Renesas Electronics Corporation.
The newly unveiled Sparrow Hawk Single Board Computer (SBC) and Raptor System on Module (SoM) are both powered by the latest Renesas R-Car V4H System-on-Chip (SoC), delivering up to 30 TOPS (Dense) of AI inference performance. These open platforms are designed to support a wide range of embedded edge AI applications and smart automotive solutions.
Sparrow Hawk focuses on robotics, industrial automation, and rapid prototyping, offering a highly flexible and cost-effective development platform. Raptor, with its modular design and multi-camera processing capabilities, is engineered for commercial vehicles, advanced driver-assistance systems (ADAS), and autonomous guided vehicles (AGVs), meeting demanding requirements for reliability and AI edge computing.
Product Highlight 1: Sparrow Hawk — A Versatile Platform for Edge AI Applications
Sparrow Hawk is a compact and highly expandable edge AI development board featuring the Renesas R-Car V4H SoC. It offers up to 30 TOPS of dense AI inference performance and supports a fully open-source Linux environment, accelerating the development of industrial and embedded AI solutions.
Key Features:*Optimized for Edge Intelligence: Designed for industrial robots, smart manufacturing, and autonomous control systems.
*Raspberry Pi HAT Compatible: Easily integrates with popular modules and sensors to streamline development.
*High AI Performance: Handles real-time image processing and AI workloads with ease thanks to 30 TOPS deep learning capabilities.
*Open Development Environment: Built on an open-source Linux architecture with extensive community support.
*Developer-Friendly Pricing: Campaign program at only USD 300, no paper contract required to get started.
*Compact Design: Measures just 146mm x 90mm, ideal for embedded and terminal devices.
Rich I/O and Expansion Interfaces:*8GB / 16GB LPDDR5 memory
*Dual-camera interface and 40-pin GPIO header
*1x DisplayPort, PCIe (4x USB3.0, 1x M.2 Key-M), 2x CAN-FD, Audio (2x In. 1x Out) and AVB Ethernet
*Supports USB PD 20V power input and MicroSD removable storage
Retronix Sparrow Hawk
Photo: Company
Product Highlight 2: Raptor — Automotive-Grade AI SoM for Smart Vehicle Vision Processing
Raptor is a high-performance SoM designed for automotive vision processing and edge AI computation. Powered by the Renesas R-Car V4H SoC, it supports multiple camera inputs, pre-processing, and AI inference. Raptor is ideal for applications including ADAS, smart cockpits, surround-view systems, and AGVs.
Key Features: *Automotive-Grade Architecture: Built with safety-oriented design principles, long-term supply, and compliance with automotive standards.
*Multi-Camera Support: Integrated ISP supports up to 8 video channels with synchronized vision processing.
*Powerful AI and Specialized Automotive IP: Delivers 30 TOPS AI inference performance with integrated Image Rendering Unit, Dense Optical Flow, Structure from Motion, and CV/Deep Learning Engines.
*Reference Carrier Design & Custom Development: Retronix offers reference designs and engineering services to accelerate product development.
*Comprehensive Software Resources: Compatible with Yocto Linux and includes the Renesas AI Hybrid Compiler toolkit.
*High Reliability: Designed for high-temperature environments with optimized power efficiency for automotive use.
Retronix Raptor
Photo: Company
Availability and Computex Showcase
Sparrow Hawk and Raptor are scheduled to sample in late Q2 2025, alongside the launch of a developer program and open-source community support platform to help users rapidly prototype and deploy AI applications.
We warmly invite industry professionals to visit Retronix at Computex 2025 (Booth N0814 / B-4) to experience the capabilities of Sparrow Hawk and Raptor firsthand. Explore their architectures, image processing performance, and AI inference efficiency across smart manufacturing, robotics, unmanned vehicles, and intelligent automotive applications.
Article edited by Jack Wu
