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git revert a495b80 -m 1 git revert f69e809 -m 1 git revert 6671653 git revert 95c06cf git revert b7285e7 git revert 67d68f6 Signed-off-by: ZHANG Yuntian <[email protected]>
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‎.github/workflows/deploy.yaml

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- name: Build
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run: |
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yarn install
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yarn build
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- name: Upload artifact

‎i18n/en/docusaurus-plugin-content-docs/current/e/e54c/README.md

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---
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sidebar_position: 50
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sidebar_label: E54C
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---
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# Radxa E54C - High-Performance AI Edge Network Computing Device
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## Product Overview
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Radxa E54C is a high-performance edge AI network computing device designed for smart routing, network storage, edge computing, and AI applications. It features a compact design that integrates powerful networking capabilities and computing power, making it an ideal platform for various network applications and AI edge computing.
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Based on the Rockchip RK3582 processor, E54C provides four gigabit Ethernet ports, flexible storage expansion options, and a rich set of interfaces to meet a wide range of needs from simple home networks to complex commercial applications.
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## Product Appearance
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<Tabs queryString="e54cmode">
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<TabItem value="E54C(with case)">
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<img src="/img/e/e54c/radxa-e54c.webp" width="800" alt="Radxa E54C with aluminum case" />
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<div style={{textAlign: 'center', marginTop: '10px', marginBottom: '20px', fontSize: '14px', color: '#666'}}>E54C aluminum case version provides efficient heat dissipation and robust protection</div>
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</TabItem>
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<TabItem value="E54C(board only)">
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<img src="/img/e/e54c/radxa-e54c-board.webp" width="800" alt="Radxa E54C board only version" />
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<div style={{textAlign: 'center', marginTop: '10px', marginBottom: '20px', fontSize: '14px', color: '#666'}}>E54C board-only version shows all interfaces and component layout</div>
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</TabItem>
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</Tabs>
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## Port Layout
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<img src="/img/e/e54c/e54c-ports-en.webp" width="800" alt="Radxa E54C port layout" />
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## Who is E54C Designed For?
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E54C is designed for:
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- **Network Engineers and IT Professionals**: Requiring high-performance multi-port network devices for network planning and management
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- **System Integrators**: Seeking reliable network computing platforms for custom solution development
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- **Businesses and Small Offices**: Needing stable and efficient network infrastructure
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- **AI Developers**: Utilizing the built-in NPU to develop and deploy edge AI applications
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- **Smart Home Enthusiasts**: Building advanced networking and automation systems
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## Purchase Options
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Radxa E54C is available in multiple configurations to meet different needs:
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| **Configuration** | **Recommended Use Case** |
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| --------------------- | --------------------------------------------------------------------- |
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| 2GB RAM + No eMMC | Entry-level routing applications, basic network management |
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| 4GB RAM + 32GB eMMC | Home/small office router, NAS server |
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| 8GB RAM + 64GB eMMC | Edge AI computing, multi-WAN load balancing, enterprise-level routing |
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| 16GB RAM + 128GB eMMC | Video analytics, edge servers, high-performance computing |
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All models can be expanded with an M.2 NVMe SSD for storage, providing higher storage performance and reliability.
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## Key Features
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- **Powerful Processing Performance**: Rockchip RK3582 processor, dual-core Cortex-A76 (up to 2.4GHz) and quad-core Cortex-A55 (up to 1.8GHz) 64-bit processor
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- **Rich Network Connectivity**: Equipped with 4 gigabit Ethernet ports, supporting multi-WAN access, load balancing, and complex network topologies
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- **Flexible Storage Solutions**: Supports MicroSD card boot, optional onboard eMMC, and M.2 NVMe SSD high-speed storage expansion
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- **AI Acceleration Capability**: Built-in NPU with computing power up to 5TOPs@INT8, supporting mainstream AI frameworks and inference tasks
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- **Multi-functional Interfaces**: Provides USB 3.0/2.0 interfaces, HDMI 2.1 output (supporting up to 8K), and GPIO expansion interface
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- **Efficient Heat Dissipation Design**: Aluminum alloy case provides excellent passive cooling, with optional fan for active cooling
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- **Multi-System Support**: Fully compatible with Debian, iStoreOS, OpenWrt, and other Linux systems
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- **Low Power Design**: Optimized power consumption while maintaining high performance, suitable for 24/7 continuous operation
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## Technical Specifications
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| **Category** | **Specifications** |
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| ------------------------- | -------------------------------------------------------------------------------------------- |
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| **SoC** | Rockchip RK3582, dual-core Cortex-A76 (up to 2.4GHz) and quad-core Cortex-A55 (up to 1.8GHz) |
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| **NPU** | Computing power up to 5TOPs@INT8, supporting mainstream AI inference frameworks |
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| **Memory** | 2GB/4GB/8GB/16GB/32GB LPDDR4 |
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| **Onboard Storage** | Optional eMMC: 8GB/16GB/32GB/64GB/128GB |
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| **Storage Expansion** | MicroSD card slot + M.2 M-Key interface (PCIe 2.1), supporting NVMe SSD |
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| **Ethernet** | 4 gigabit Ethernet ports (WAN/LAN configurable) |
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| **USB Ports** | 1 USB 3.0 Type-A + 2 USB 2.0 Type-A + 1 USB 3.0 Type-C (OTG) |
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| **Video Output** | HDMI 2.1 interface, supporting up to 8K resolution |
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| **GPIO** | 14-pin expansion header, supporting SPI, UART, I2C, and power output |
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| **Other Interfaces** | Fan connector, RTC battery connector, power button, Maskrom button, user-defined button |
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| **Power Supply** | DC 12V/2A, 5.5 x 2.5mm connector |
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| **Operating Systems** | Supports Debian, iStoreOS, OpenWrt, and other Linux systems |
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| **Operating Temperature** | 0°C ~ 70°C (standard version) |
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| **Dimensions** | 130 x 85 x 24mm (with case version) |
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## Hardware Interface Layout
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<img src="/img/e/e54c/radxa-e54c-hardware-overview.webp" width="800" alt="E54C hardware interface layout" />
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The above image shows the main hardware interfaces and component layout of E54C, including:
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1. Four gigabit Ethernet interfaces (RJ45)
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2. USB 3.0/2.0 ports
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3. HDMI 2.1 output port
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4. M.2 NVMe SSD slot
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5. MicroSD card slot
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6. GPIO expansion interface
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7. 12V DC power connector
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## System Support
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E54C supports multiple operating systems that can be chosen based on different application scenarios:
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### Debian Linux
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A complete Linux desktop environment, suitable for general computing and development needs:
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- Provides a complete package management system
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- Supports mainstream programming languages and development tools
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- Suitable for application development and testing
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### iStoreOS
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An intelligent router operating system based on OpenWrt, optimized for network applications:
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- Graphical web management interface, easy to configure
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- Rich network features and application plugins
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- Supports advanced features such as multi-WAN load balancing and smart QoS
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### OpenWrt
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An open-source router operating system, suitable for highly customized network needs:
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- Lightweight system, efficient operation
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- Flexible network configuration
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- Supports various network services and security features
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## Application Scenarios
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### Smart Network Solutions
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- **Multi-WAN Smart Router**: Utilize four gigabit network ports to build multi-WAN access, implementing load balancing and link backup
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- **Advanced Firewall**: Configure fine-grained access control and traffic monitoring to ensure network security
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- **VPN Server/Client**: Support mainstream VPN protocols such as OpenVPN and WireGuard to build secure remote access
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- **QoS Traffic Control**: Optimize bandwidth allocation to ensure critical applications get priority
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### Edge AI Applications
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- **Intelligent Monitoring System**: Utilize the built-in NPU for video analysis and object detection
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- **Predictive Maintenance**: Process sensor data for edge analysis and anomaly detection
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- **Natural Language Processing**: Deploy lightweight speech recognition and processing models
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- **Machine Vision**: Implement computer vision applications such as image classification and object detection
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### Network Storage and Multimedia
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- **NAS Server**: Build high-performance network storage solutions using M.2 NVMe SSD
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- **Media Server**: Set up media servers such as Plex and Jellyfin
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- **Content Caching**: Deploy local content caching to optimize network traffic
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### IoT Management
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- **Smart Home Control Center**: Manage various smart home devices and automation scenarios
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- **IoT Gateway**: Connect and manage multiple IoT devices and sensors
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- **Data Collection and Analysis**: Collect sensor data and perform local processing and analysis
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<DocCardList />

‎i18n/en/docusaurus-plugin-content-docs/current/e/e54c/_image.mdx

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import React, { Fragment } from "react";
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<ul>
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<li style={{ display: `${props.loader ? "block" : "none"}` }}>
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{" "}
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<a href="https://dl.radxa.com/e/e54c/images/rk3582_spl_loader_v1.15.113.bin">
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{" "}
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Loader: rk3582_spl_loader_v1.15.113.bin{" "}
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</a>
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</li>
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<li style={{ display: `${props.system_img ? "block" : "none"}` }}>
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{" "}
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<a href="https://dl.radxa.com/e/e54c/images/istoreos-22.03.7-2024101020-e54c-squashfs.img.gz">
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{" "}
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system image: istoreos-22.03.7-2024101020-e54c-squashfs{" "}
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</a>
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</li>
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</ul>

‎i18n/en/docusaurus-plugin-content-docs/current/e/e54c/app-development/README.md

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---
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sidebar_position: 8
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---
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# Application Development
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This section mainly introduces upper-layer application development, such as AI applications, NPU development, etc.
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<DocCardList />

‎i18n/en/docusaurus-plugin-content-docs/current/e/e54c/app-development/artificial-intelligence/README.md

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---
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sidebar_position: 1
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---
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# Artificial Intelligence
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This section mainly introduces application development using NPU for hardware-accelerated artificial intelligence
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<DocCardList />

‎i18n/en/docusaurus-plugin-content-docs/current/e/e54c/app-development/artificial-intelligence/rk3582_npu_explanation.md

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---
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description: "RK3582 NPU Platform Specific Instructions"
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---
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# RK3582 NPU Platform Specific Instructions
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import Rk3582npuexplantion from '../../../../common/dev/\_rk3586_npu_explanation.mdx';
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<Rk3582npuexplantion />

‎i18n/en/docusaurus-plugin-content-docs/current/e/e54c/app-development/artificial-intelligence/rkllm_deepseek_r1.md

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---
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sidebar_position: 9
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description: "Running DeepSeek-R1 large language model with RKLLM"
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---
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# RKLLM DeepSeek-R1
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import RKLLMDEEPSEEKR1 from '../../../../common/dev/\_rkllm-deepseek-r1.mdx';
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<RKLLMDEEPSEEKR1 />

‎i18n/en/docusaurus-plugin-content-docs/current/e/e54c/app-development/artificial-intelligence/rkllm_install.md

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---
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sidebar_position: 7
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description: "Through RKLLM installation, embark on an efficient inference journey of LLM models on Rockchip NPU, and experience the perfect fusion of technology and humanity"
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---
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# RKLLM Installation
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import Rkllminstall from '../../../../common/dev/\_rkllm-install.mdx';
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<Rkllminstall />

‎i18n/en/docusaurus-plugin-content-docs/current/e/e54c/app-development/artificial-intelligence/rkllm_usage.md

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---
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sidebar_position: 8
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description: "Detailed RKLLM usage documentation"
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---
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# RKLLM Usage and Large Language Model Deployment
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import Rkllmusage from '../../../../common/dev/\_rkllm-usage.mdx';
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<Rkllmusage />

‎i18n/en/docusaurus-plugin-content-docs/current/e/e54c/app-development/artificial-intelligence/rknn_install.md

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---
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sidebar_position: 1
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description: "Start your efficient AI model inference journey on Rockchip NPU through RKNN installation, and experience the perfect fusion of technology and humanity"
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---
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# RKNN Installation
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import RKNNinstall from '../../../../common/dev/\_rknn-install.mdx';
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<RKNNinstall />

‎i18n/en/docusaurus-plugin-content-docs/current/e/e54c/app-development/artificial-intelligence/rknn_quick_start.md

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---
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sidebar_position: 2
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description: "Verify NPU availability through simple examples"
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---
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# RKNN Quick Examples
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import Rknntoolkit2 from '../../../../common/dev/\_rknn-toolkit2.mdx';
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<Rknntoolkit2 />

‎i18n/en/docusaurus-plugin-content-docs/current/e/e54c/app-development/artificial-intelligence/rknn_toolkit2_pc.md

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---
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sidebar_position: 3
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description: "Using the RKNN toolkit to explore AI model simulation inference, experience the efficiency and precision of intelligent image segmentation"
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---
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# Simulating Inference with YOLOv5 Segmentation Model
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import RKNNTOOLKIT2PC from '../../../../common/dev/\_rknn-toolkit2-pc.mdx';
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<RKNNTOOLKIT2PC />

‎i18n/en/docusaurus-plugin-content-docs/current/e/e54c/app-development/artificial-intelligence/rknn_toolkit_lite2_yolov5.md

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---
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sidebar_position: 4
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description: "Deploy YOLOv5 on RK3582 board to usher in a new era of intelligent object detection, where technology and humanity perfectly blend in precise recognition"
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---
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# On-board Deployment of YOLOv5 Object Detection
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import RKNNTOOLKITLITE2YOLOV5 from '../../../../common/dev/\_rknn-toolkit-lite2-yolov5.mdx';
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<RKNNTOOLKITLITE2YOLOV5 />

‎i18n/en/docusaurus-plugin-content-docs/current/e/e54c/app-development/artificial-intelligence/rknn_toolkit_lite2_yolov8.md

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---
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description: "Deploy YOLOv8 on RK3582 board to usher in a new era of intelligent object detection, where technology and humanity perfectly blend in precise recognition"
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---
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# On-board Deployment of YOLOv8 Object Detection
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import RKNNTOOLKITLITE2YOLOV8 from '../../../../common/dev/\_rknn-toolkit-lite2-yolov8.mdx';
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<RKNNTOOLKITLITE2YOLOV8 />

‎i18n/en/docusaurus-plugin-content-docs/current/e/e54c/app-development/artificial-intelligence/rknn_ultralytics.md

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---
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sidebar_position: 6
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description: "Deploy YOLOv11 on RK3582 board to usher in a new era of intelligent object detection, where technology and humanity perfectly blend in precise recognition"
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---
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# RKNN Ultralytics YOLOv11
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import RKNNULTRALYTICS from '../../../../common/dev/\_rknn-ultralytics.mdx';
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<RKNNULTRALYTICS />

‎i18n/en/docusaurus-plugin-content-docs/current/e/e54c/app-development/artificial-intelligence/venv_usage.md

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---
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sidebar_position: 22
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description: "Using virtual environments to isolate system environments"
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---
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# Python Virtual Environment Usage
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import VENVUSAGE from '../../../../common/dev/\_venv_usage.mdx';
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<VENVUSAGE />

‎i18n/en/docusaurus-plugin-content-docs/current/e/e54c/download.md

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---
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---
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import Images from "./\_image.mdx"
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# Resource Downloads
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## Operating System Images
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### iStoreOS:
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iStoreOS image file, can be directly written to SD card / eMMC / SSD after extraction
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[istoreos-22.03.7-2025040711-e54c-squashfs.img.gz](https://fw0.koolcenter.com/iStoreOS/e54c/istoreos-22.03.7-2025040711-e54c-squashfs.img.gz)
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### Debian:
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Debian image file, can be directly written to SD card / eMMC / SSD after extraction
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[radxa-e54c_bookworm_cli_b2.output.img.xz](https://github.com/radxa-build/radxa-e54c/releases/download/rsdk-b2/radxa-e54c_bookworm_cli_b2.output.img.xz)
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### Loader Files
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Used for USB flashing, Loader file is used for USB download initialization, no need to download when writing to SD card
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[rk3588_spl_loader_v1.15.113.bin](https://dl.radxa.com/e/e52c/images/rk3588_spl_loader_v1.15.113.bin)
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## Hardware Design
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Radxa E54C V1.2 version
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- [V1.2 Component Placement Map (PDF)](https://dl.radxa.com/e/e54c/hw/radxa_e54c_v1.2_components_placement_map.pdf)
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- [V1.2 Schematic (PDF)](https://dl.radxa.com/e/e54c/hw/radxa_e54c_v1.2_schematic.pdf)
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## Quality Certification
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## Reference Documents
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- [E54C Feature Map](https://dl.radxa.com/e/e54c/docs/Radxa_E54C_quick_features_en.pdf)
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- [E54C Specification](https://dl.radxa.com/e/e54c/docs/radxa_e54c_product_brief_Revision_1.0_g37b8f72.pdf)
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- [E54C User Guide](https://dl.radxa.com/e/e54c/docs/Radxa_E54C_Quick_User_Guide_en.pdf)
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## 3D Files
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### Third-party Cases

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