{"product_id":"radxa-aicore-dx-m1-1","title":"RADXA AICore DX-M1","description":"\u003cp\u003eRadxa AICore DX‑M1 is a high‑performance M.2 acceleration module based on DEEPX’s DX‑M1 module. It features high energy efficiency, high‑precision\u003cbr\u003ecomputing, and ease of use, specifically optimized for edge computing scenarios and\u003cbr\u003emachine vision.\u003c\/p\u003e\n\u003cp\u003eThe Radxa AICore DX‑M1 module is currently compatible with various single‑board com‑\u003cbr\u003eputers (SBCs), such as Radxa ROCK 5B\/5B+ and other motherboards.\u003c\/p\u003e\n\u003cp\u003e\u003c!-- split --\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003e\u003c\/strong\u003e\u003c\/span\u003e\u003cspan\u003e\u003cstrong\u003eRADXA AICore DX-M1 product details\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003eDEEPX’s DX‑M1 module\u003c\/p\u003e\n\u003cp\u003eOcta‑core Cortex‑A55 processor with a frequency up to 1.5GHz\u003c\/p\u003e\n\u003cp style=\"padding-left: 30px;\"\u003e\u003cspan\u003e\u003cspan class=\"fontstyle0\"\u003eNPU – 24TOPS@INT8, Supports Matrix Arithmetic Unit (MAU) and Intelligent\u003cbr\u003eVideo Engine (IVE)\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp style=\"padding-left: 30px;\"\u003e\u003cspan\u003e\u003cspan class=\"fontstyle0\"\u003eVPU – Supports H.264\/H.265 8K@30fps codec and 16‑channel 1080p@30fps\u003cbr\u003edecoding\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cspan class=\"fontstyle0\"\u003e\u003cstrong\u003eAI Performance\u003c\/strong\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e25 TOPS\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cspan class=\"fontstyle0\"\u003e\u003cstrong\u003eMemory\u003c\/strong\u003e\u003cbr\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e4GB LPDDR5 + 1Gbit QSPI NAND Flash\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eStorage\u003c\/strong\u003e\u003cstrong\u003e \u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cspan class=\"fontstyle0\"\u003eQSPI 1Gbit NAND \/ NOR Flash\u003cbr\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cstrong class=\"fontstyle0\"\u003eHost Interface\u003c\/strong\u003e\u003cbr\u003e\u003c\/p\u003e\n\u003cp\u003eM.2 M Key\u003c\/p\u003e\n\u003cp\u003e\u003cstrong class=\"fontstyle0\"\u003eHost System\u003c\/strong\u003e\u003cbr\u003e\u003c\/p\u003e\n\u003cp\u003eSupports mainstream host platforms including ARM, x86\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eSoftware\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan class=\"fontstyle0\"\u003e・ Support for Ubuntu, Debian\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eAI Frameworks\u003c\/strong\u003e\u003cbr\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan style=\"font-size: 0.875rem;\"\u003e\u003cspan class=\"fontstyle0\"\u003e・ TensorFlow\u003cbr\u003e・ ONNX\u003cbr\u003e・ PyTorch\u003cbr\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eOperating Temperature\u003c\/strong\u003e\u003cbr\u003e\u003c\/p\u003e\n\u003cp\u003e‑25 ~ 65°C (Non‑Throttling) \/ ‑25 ~ 85°C (Throttling)\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eHost Platform\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eSupports mainstream host platforms including Intel, AMD, Rockchip,\u003cbr\u003eetc.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003ePower Consumption\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan class=\"fontstyle0\"\u003e@3.3V ≤ 5W\u003cbr\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003e\u003cu\u003e\u003c\/u\u003e\u003c\/strong\u003e\u003c\/span\u003e\u003cstrong\u003eForm Factor\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan style=\"font-size: 0.875rem;\"\u003eM.2 M Key, 2280 (22mm x 80mm)\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cimg\u003e\u003cimg height=\"65\" width=\"295\" alt=\"\" src=\"https:\/\/cdn.shopify.com\/s\/files\/1\/0021\/1497\/7894\/files\/Rohrs_CE_1.png?v=1779939667\"\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e______________________________________________________\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan\u003eAdditional information \u0026amp; Setup instructions\u003c\/span\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eunder preparation\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e______________________________________________________\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eAvailable until at least July 2030\u003c\/span\u003e\u003c\/p\u003e","brand":"RADXA","offers":[{"title":"Default Title","offer_id":53091504357692,"sku":"RM192","price":120.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0021\/1497\/7894\/files\/Top_1_2.jpg?v=1780392837","url":"https:\/\/shop.allnetchina.cn\/products\/radxa-aicore-dx-m1-1","provider":"ALLNET China","version":"1.0","type":"link"}