Rockchip RK11xx Category - CNX Software

April 20, 2026April 19, 2026 by Debashis Das - 1 Comment

IP67-rated AI security camera feature Rockchip RV1126B or RK3576/J/M SoC for commercial, industrial, and automotive applications

Firefly CQ38W 1126B and CQ38W 3576 AI smart security cameras

Back in January 2024, Firefly released the CT36L AI smart security cameras, built around the Rockchip RV1106G2 SoC with a 0.5 TOPS NPU and Power over Ethernet (PoE) support. Now, Firefly has introduced two new AI cameras, the CQ38W-1126B and CQ38W-3576, which use a similar IP67-rated enclosure but come with much more powerful processors. Both new models no longer support PoE and instead use a 12V DC input, and they also add an RS485 interface. In terms of performance, the CQ38W-1126B is built around the Rockchip RV1126B with a 3 TOPS NPU and can run small multimodal AI models. The higher-end CQ38W-3576 features an octa-core Rockchip RK3576 with a 6 TOPS NPU, making it suitable for more demanding AI workloads, including YOLO and large language models. Both cameras are available with 3MP or 5MP sensors and come in Commercial or Industrial (J-suffix) variants. The 3576 series also adds an Automotive-grade […]

July 23, 2025 by Jean-Luc Aufranc (CNXSoft) - 3 Comments

QNAP QAI-M100 and QAI-U100 Edge AI accelerators for NAS systems improve image recognition performance

QNAP QAI-M100 QAI-U100 NAS AI Accelerators

NAS used to store your files in your local network for easy access, but new models like QNAP TS-216G, TS-AI642, and TS-133 feature an Arm processor (Rockchip RK3566 or RK3588) with a built-in NPU to accelerate image recognition, so that users can find their photos with a simple text search like “Disneyland Mickey Mouse”. However, if you own an older NAS that lacks an AI accelerator, QNAP has launched the QAI-M100 (M.2 2280) and QAI-U100 (USB) equipped with a Rockchip RK1808 3 TOPS AI accelerator designed to speed up image and facial recognition by up to 36%. QNAP QAI-M100/QAI-U100 specifications: SoC – Rockchip RK1808 CPU – Dual-core Arm Cortex-A35 @ 1.6 GHz NPU – 3 TOPS NPU Memory – 1GB RAM Host interface QAI-M100 – M.2 2280 PCIe Gen2 x1 (M+B key) edge connector QAI-U100 – USB 3.2 Gen1 male port Accessories – Installation guide and heatsink (QAI-M100 only) Dimensions / […]

July 27, 2022 by Jean-Luc Aufranc (CNXSoft) - 4 Comments

mini PCIe module features Rockchip RK1808K SoC with 3.0 TOPS NPU

Toybrick RK1808 mPCIe AI accelerator card

Rockchip RK1808 SoC with a built-in 3.0 TOPS AI accelerator has been around since 2019, and we’ve seen it in USB compute sticks, SBCs, and even in Pine64 SoEdge-RK1808 SO-DIMM module, but somehow never in the more widely used M.2 or mPCIe form factors. Toybrick TB-RK1808M0 changes that and offers Rockchip RK1808K SoC coupled with 1GB RAM and an 8GB eMMC flash in a mini PCIe module that exposes USB 3.0, USB 2.0, UART, and GPIO signals. Toybrick TB-RK1808M0 specifications: SoC – Rockchip RK1808K CPU – Dual-core Cortex-A35 processor @ up to 1.4 GHz AI Accelerator – 3.0 TOPS NPU for INT8 inference (300 GOPS for INT16, 100 GFLOPS for FP16) VPU – 1080p60 H.264 decode, 1080p30 H.264 encode System Memory – 1GB DDR Storage – 8GB eMMC flash Host interface – Mini PCIe edge connector with USB 3.0, USB 2.0, UART, and GPIO Misc – Heatsink for cooling Supply […]

April 17, 2020 by Jean-Luc Aufranc (CNXSoft) - No Comments

Boardcon RK1808 SBC Targets Smart Audio & Computer Vision Applications

Rockchip RK1808 neural network processing unit was initially an IP Block inside RK3399Pro, but the company eventually launched RK1808 Cortex-A35 processor as a standalone solution now providing up to 3.0 TOPS for AI inferencing in modules, USB sticks, and development kits. Boardcon offers another option with EM1808, a Rockchip RK1808 SBC equipped with the processor. The board should be suitable for two main types of AI applications, namely smart audio applications thanks to four audio ports, speaker header, & an onboard 4-mic array, and computer vision with MIPI CSI & DSI interfaces. Boardcon EM1808 board is comprised of a baseboard and CPU module with the following overall specifications: SoC – Rockchip RK1808 dual Cortex-A35 processor up to 1.6GHz with 3.0 TOPS (for INT8) NPU, VPU supporting H.264 1080p60 decode, 1080p30 encode System Memory- 2GB LPDDR3 Storage – 8GB eMMC flash, MicroSD slot, M.2 NVMe SSD interface Display I/F – 26-pin […]

August 20, 2019July 27, 2022 by Jean-Luc Aufranc (CNXSoft) - 5 Comments

Toybrick TB-RK1808 AI Compute Stick is now Available for $86

Last May, we wrote about RK1808 AI Compute Stick, a USB stick with Rockchip RK1808 dual-core Cortex-A35 processor also featuring a 3.0 TOPS neural processing unit to accelerate AI workloads at low power. As I understood it, it was available for purchase, but you had to contact a Rockchip FAE by email in order to get one. Now, you can easily buy one online by getting the Toybrick TB-RK1808 AI Compute Stick on Seeed Studio for $86. Just ignore the “Core i3” in the title, we’ll see why it’s there further below. TB-RK1808 AI Compute Stick specifications: SoC – Rockchip RK1808 dual-core Cortex-A35 processor with NPU AI inference performance – 3 TOPS for INT8, 300 GOPS for INT16, 100 GOPS for FP16 System Memory – 1GB LPDDR Storage – 8GB eMMC flash Host Interface – USB 3.0 type-A port Power Supply – Via USB port Dimensions – 82 x 31 […]

August 5, 2019August 5, 2019 by Jean-Luc Aufranc (CNXSoft) - 6 Comments

Pine64 SoEdge-RK1808 AI Module Delivers 3.0 TOPS via Rockchip RK1808 SoC

SOPINE Model A Baseboard + SoEdge-RK1808

A few weeks ago, Ameridroid reported Pine64 would soon launch SoRock and SoEdge systems-on-module, but at the time there was virtually no info except SoRock would be likely based on either RK3328 or RK3399 and work on the existing Clusterboard, while SoEdge would be an AI Neural module for Artificial Intelligence tasks, with up to 3 TeraFLOPS of performance. I did not write about it at the time, simply because there was so little information, but this morning I’ve just received some photos of SoEdge-RK1808 module fitted to a baseboard that looks to be SOPINE Model “A” carrier board. SoEdge-RK1808 SoM Let’s try to derive the specifications from the photos even though some components appear to be blurred out or just unclear: SoC – Rockchip RK1808 dual-core Cortex-A35 processor with 3.0 TOPS NPU (Neural Processing Unit) System Memory – 2GB RAM (2x 8GBit Micro DDR4-2400) but limited PC-2133 Storage – […]

July 1, 2019December 4, 2019 by Jean-Luc Aufranc (CNXSoft) - 4 Comments

96Boards RK1808 & RK3399Pro SoM & Devkit Now Available for Purchase

Back in April, we covered the very first 96Boards SoM’s (Systems-on-Module) which were based on Rockchip RK3399Pro or RK1808 processors, and targeted applications leveraging artificial intelligence acceleration. There were not quite available at the time, but Seeed Studio now has both BeiQi modules for pre-order for $119 and $59 respectively, while the carrier board goes with $125 with antennas, and power supply. Note that the RK3399Pro SoM and the carrier board are basically available now with shipping schedule for July 4th, but you’d had to wait until the end of the month for the RK1808 module. BeiQi RK1808 AIoT 96Boards Compute SoM Module specifications: SoC – Rockchip RK1808 dual-core Arm Cortex-A35 processor @ 1.6 GHz with NPU supporting 8-bit/16-bit operations up to 3.0 TOPS, TensorFlow and Caffe frameworks; 22nm FD-SOI process System Memory – 1GB LPDDR3 (I also read “4GB LPDRR3” (sic.) in other places, but the capacity is likely […]

May 15, 2019 by Jean-Luc Aufranc (CNXSoft) - 12 Comments

ToyBrick RK3399Pro Board Shown to Outperform Jetson Nano SBC

NVIDIA created a lot of buzz when they released $99 Jetson Nano SBC featuring a 128-core Maxwell GPU, and said to deliver 472 GFLOPS of compute performance for running modern AI workloads with a power consumption of around 5 watts. But Jetson Nano is not the only low cost platform to deliver high performance at low power for AI workloads, as for example Rockchip RK3399Pro (RK1808 NPU) found in boards such as Toybrick RK3399Pro is said to deliver 3 TOPS for INT8, 300 GOPS for INT16, and 100 GOPS for FP16 inferences. Those operations per second numbers can be confusing and misleading, so it’s important to check out the performance of actual neural network models, and Rockchip did provide some RK3399Pro benchmarks last year for Inception V3, ResNet34 and VGG16 models comparing the results to Apple A11, Huawei Kirin 970, and NVIDIA Jetson TX2. However, ideally you’d want result from […]