{"title":"AI Single-Board Computer","description":null,"products":[{"product_id":"mo-62a-single-board-computer-2-tops-open-source-ai-sbc-for-lightweight-edge-inference","title":"Mo 62A Single-Board Computer 2 TOPS Open-Source AI SBC For Lightweight Edge Inference","description":"\u003cdiv class=\"mo62a\"\u003e\n\u003cstyle\u003e\n\/* 隐藏产品模板自动生成的外层标签栏（Description|Reviews），避免与本标签栏重复 *\/\n#shopify-section-product-template div.price.tab \u003e div.category{display:none}\n\n.mo62a{--green:#00a651;--green-deep:#00873f;--ink:#1c1b1b;--text:#4a5459;--muted:#677279;--line:#e5e5e5;\n  font-family:-apple-system,BlinkMacSystemFont,\"Segoe UI\",Roboto,\"Helvetica Neue\",Arial,sans-serif;\n  color:var(--text);font-size:15px;line-height:1.7;width:100%}\n.mo62a *{box-sizing:border-box}\n.mo62a img{max-width:100%}\n.mo62a h2,.mo62a h3{color:var(--ink);margin:0;padding:0}\n.mo62a p{margin:0;padding:0}\n\n\/* 标签栏（FWA12\/ODU302 同款视觉） *\/\n.mo62a 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\u003cli\u003eReviews\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003c!-- ============ TAB 1 · Description ============ --\u003e\n\u003cdiv class=\"ocont active\"\u003e\n\n  \u003c!-- Overview --\u003e\n  \u003cdiv class=\"oblock\"\u003e\n    \u003ch3 class=\"otitle\" style=\"margin-top:0\"\u003eOverview\u003c\/h3\u003e\n    \u003ch2 style=\"text-align:center\"\u003eInHand Networks Mo 62A AI Single Board Computer\u003c\/h2\u003e\n    \u003cp class=\"odetail\"\u003eThe Mo 62A is an edge-AI single board computer built on the TI AM62A74 vision processor. With a dedicated C7x DSP and deep-learning accelerator delivering 2 TOPS of on-device inference, an on-chip ISP engineered for real-world camera scenes, and a standard SBC form factor compatible with the HAT ecosystem, the Mo 62A turns AI vision ideas into deployable edge devices — at a fraction of the cost and power of traditional AI hardware. Running Debian Linux with an open SDK and TI TIDL support for TFLite\/ONNX models, it is built for developers who want to go from prototype to production — with the complete developer environment, tools and examples published on GitHub, the open-source way.\u003c\/p\u003e\n    \u003c!-- 图片位 01 · 主视觉横幅 --\u003e\n    \u003cdiv class=\"ph ph-banner\"\u003e\n\u003cb\u003e图片位 01 · 主视觉横幅 · 1536×620\u003c\/b\u003e建议：Mo 62A 板卡正视大图 + 边缘 AI 视觉场景背景（工地\/路口\/产线虚化）+ 标题文案\u003c\/div\u003e\n  \u003c\/div\u003e\n\n  \u003c!-- Features --\u003e\n  \u003cdiv class=\"oblock\"\u003e\n    \u003ch3 class=\"otitle\"\u003eFeatures\u003c\/h3\u003e\n\n    \u003cdiv class=\"feat\"\u003e\n      \u003ch2\u003e2 TOPS of Edge AI — Inference Where the Camera Is\u003c\/h2\u003e\n      \u003cp\u003eA dedicated C7x DSP with Matrix Multiply Accelerator delivers 2 TOPS of deep-learning performance on-device. Detection, classification and segmentation models run locally — no cloud round-trips, no bandwidth bills, no privacy exposure. TI TIDL toolchain deploys your existing TFLite and ONNX models with minimal effort.\u003c\/p\u003e\n      \u003c!-- 图片位 02 · 特性1 --\u003e\n      \u003cdiv class=\"ph ph-banner\"\u003e\n\u003cb\u003e图片位 02 · 特性横幅 · 1536×620\u003c\/b\u003e建议：AI 推理视觉化（摄像头画面叠加检测框\/识别结果），文案如 \"2 TOPS Edge AI\"\u003c\/div\u003e\n    \u003c\/div\u003e\n\n    \u003cdiv class=\"feat\"\u003e\n      \u003ch2\u003eA Vision Processor, Not Just an SBC\u003c\/h2\u003e\n      \u003cp\u003eUnlike general-purpose boards, the AM62A integrates an on-chip ISP with VPAC vision acceleration — hardware WDR, lens distortion correction and RGB-IR support. Backlit entrances, high-contrast construction sites and low-light scenes are handled in silicon before your model ever sees the frame, lifting real-world detection accuracy.\u003c\/p\u003e\n      \u003c!-- 图片位 03 · 特性2 --\u003e\n      \u003cdiv class=\"ph ph-banner\"\u003e\n\u003cb\u003e图片位 03 · 特性横幅 · 1536×620\u003c\/b\u003e建议：逆光\/高对比场景下普通画面 vs WDR 画面左右对比，文案如 \"Hardware WDR · LDC · RGB-IR\"\u003c\/div\u003e\n    \u003c\/div\u003e\n\n    \u003cdiv class=\"feat\"\u003e\n      \u003ch2\u003eRaspberry Pi Form Factor, Industrial AI Core\u003c\/h2\u003e\n      \u003cp\u003eStandard 85 × 56 mm SBC footprint with a 40-pin HAT-compatible header (GPIO \/ I²C \/ SPI \/ UART \/ PCM). Mounts in mainstream SBC enclosures and works with the vast ecosystem of HAT expansion boards — PoE HATs, relays, sensors, displays — so prototyping hardware is never a blocker.\u003c\/p\u003e\n      \u003c!-- 图片位 04 · 特性3 --\u003e\n      \u003cdiv class=\"ph ph-banner\"\u003e\n\u003cb\u003e图片位 04 · 特性横幅 · 1536×620\u003c\/b\u003e建议：板卡 + 常见 HAT\/外壳配件平铺组合图，文案如 \"Standard SBC · HAT-Compatible\"\u003c\/div\u003e\n    \u003c\/div\u003e\n\n    \u003cdiv class=\"feat\"\u003e\n      \u003ch2\u003eBuilt for Developers — Everything on GitHub\u003c\/h2\u003e\n      \u003cp\u003eA complete developer environment is published on GitHub, the open-source-community way: ready-to-flash Debian system images, the full SDK for custom builds, AI model deployment examples, pinout and device-tree documentation — clone, flash and run your first inference in minutes. Issues and contributions are handled in the open, so answers stay searchable for everyone. No proprietary runtime in the way: develop in Python or C\/C++ with OpenCV, GStreamer and NumPy, manage packages with apt, and access the board over SSH. Your code, your system, your product.\u003c\/p\u003e\n      \u003c!-- 图片位 05 · 特性4 --\u003e\n      \u003cdiv class=\"ph ph-banner\"\u003e\n\u003cb\u003e图片位 05 · 特性横幅 · 1536×620\u003c\/b\u003e建议：GitHub 仓库界面截图 + 终端部署流程，文案如 \"Everything on GitHub · Images · SDK · Examples\"\u003c\/div\u003e\n    \u003c\/div\u003e\n\n    \u003cdiv class=\"feat\"\u003e\n      \u003ch2\u003eConnectivity for Real Deployments\u003c\/h2\u003e\n      \u003cp\u003eGigabit Ethernet for reliable backhaul, four USB 2.0 ports for peripherals and additional cameras, micro HDMI for local display, dual-band Wi-Fi 5 and BLE 4.2 for cable-free installs, and a 4-lane MIPI CSI-2 interface for direct camera input — everything an edge vision terminal needs, nothing it doesn't.\u003c\/p\u003e\n      \u003c!-- 图片位 06 · 特性5 --\u003e\n      \u003cdiv class=\"ph ph-banner\"\u003e\n\u003cb\u003e图片位 06 · 特性横幅 · 1536×620\u003c\/b\u003e建议：板卡接口标注图（网线\/USB\/HDMI\/CSI 引出线框标注）\u003c\/div\u003e\n    \u003c\/div\u003e\n\n    \u003cdiv class=\"feat\"\u003e\n      \u003ch2\u003eSecure by Design\u003c\/h2\u003e\n      \u003cp\u003eSecure boot, Arm TrustZone, OP-TEE trusted execution and a hardware AES-256 crypto accelerator protect firmware, models and data end to end — essential when devices are deployed unattended in public spaces and your AI model is your IP.\u003c\/p\u003e\n      \u003c!-- 图片位 07 · 特性6 --\u003e\n      \u003cdiv class=\"ph ph-banner\"\u003e\n\u003cb\u003e图片位 07 · 特性横幅 · 1536×620\u003c\/b\u003e建议：安全盾牌\/锁 + 板卡视觉，文案如 \"Secure Boot · TrustZone · AES-256\"\u003c\/div\u003e\n    \u003c\/div\u003e\n\n    \u003cdiv class=\"feat\"\u003e\n      \u003ch2\u003eFrom Prototype to Production\u003c\/h2\u003e\n      \u003cp\u003eUSB-C 5V\/5A power, 25 W maximum consumption and fanless-friendly thermals keep deployment simple and silent. Three memory options (2GB \/ 4GB \/ 8GB) let you match cost to workload — validate on a 4GB board today, order production volumes tomorrow, on the same platform and the same software.\u003c\/p\u003e\n      \u003c!-- 图片位 08 · 特性7 --\u003e\n      \u003cdiv class=\"ph ph-banner\"\u003e\n\u003cb\u003e图片位 08 · 特性横幅 · 1536×620\u003c\/b\u003e建议：单块原型 → 批量部署的演进视觉，文案如 \"Prototype to Production\"\u003c\/div\u003e\n    \u003c\/div\u003e\n  \u003c\/div\u003e\n\n  \u003c!-- Why Mo 62A · 对比表 --\u003e\n  \u003cdiv class=\"oblock\"\u003e\n    \u003ch3 class=\"otitle\"\u003eWhy Mo 62A — Compare Before You Build\u003c\/h3\u003e\n    \u003cp class=\"odetail\"\u003eGeneral-purpose SBCs leave AI to add-ons; high-end AI kits cost several times more. The Mo 62A puts a dedicated vision AI pipeline in a standard SBC form factor — right-sized for single- and dual-camera edge inference.\u003c\/p\u003e\n    \u003cdiv class=\"tscroll\"\u003e\n      \u003ctable class=\"cmp\"\u003e\n        \u003ctr\u003e\n\u003cth\u003eSpecification\u003c\/th\u003e\n\u003cth\u003eRaspberry Pi 5\u003c\/th\u003e\n\u003cth\u003eJetson\u003cbr\u003eOrin Nano\u003c\/th\u003e\n\u003cth\u003eTypical RK3588 SBC\u003c\/th\u003e\n\u003cth\u003eInHand\u003cbr\u003eMo 68A\u003c\/th\u003e\n\u003cth class=\"us\"\u003eInHand Mo 62A\u003c\/th\u003e\n\u003c\/tr\u003e\n        \u003ctr\u003e\n\u003ctd\u003ePositioning\u003c\/td\u003e\n\u003ctd\u003eGeneral-purpose SBC\u003c\/td\u003e\n\u003ctd\u003eHigh-perf AI dev kit\u003c\/td\u003e\n\u003ctd\u003eMultimedia SBC\u003c\/td\u003e\n\u003ctd\u003eMulti-camera vision AI\u003c\/td\u003e\n\u003ctd class=\"us\"\u003eEdge AI vision SBC\u003c\/td\u003e\n\u003c\/tr\u003e\n        \u003ctr\u003e\n\u003ctd\u003eAI acceleration\u003c\/td\u003e\n\u003ctd\u003e— (requires AI HAT)\u003c\/td\u003e\n\u003ctd\u003eUp to 40 TOPS\u003c\/td\u003e\n\u003ctd\u003e6 TOPS NPU\u003c\/td\u003e\n\u003ctd\u003e8 TOPS\u003c\/td\u003e\n\u003ctd class=\"us y\"\u003e2 TOPS (C7x DSP + MMA)\u003c\/td\u003e\n\u003c\/tr\u003e\n        \u003ctr\u003e\n\u003ctd\u003eAI toolchain\u003c\/td\u003e\n\u003ctd\u003eVia HAT vendor\u003c\/td\u003e\n\u003ctd\u003eTensorRT\u003c\/td\u003e\n\u003ctd\u003eRKNN\u003c\/td\u003e\n\u003ctd\u003eTIDL · TFLite \/ ONNX\u003c\/td\u003e\n\u003ctd class=\"us y\"\u003eTIDL · TFLite \/ ONNX\u003c\/td\u003e\n\u003c\/tr\u003e\n        \u003ctr\u003e\n\u003ctd\u003eCPU\u003c\/td\u003e\n\u003ctd\u003e4× Cortex-A76 @2.4GHz\u003c\/td\u003e\n\u003ctd\u003e6× Cortex-A78AE\u003c\/td\u003e\n\u003ctd\u003e8× (A76+A55)\u003c\/td\u003e\n\u003ctd\u003e2× Cortex-A72 @2.0GHz\u003c\/td\u003e\n\u003ctd class=\"us\"\u003e4× Cortex-A53 @1.4GHz\u003c\/td\u003e\n\u003c\/tr\u003e\n        \u003ctr\u003e\n\u003ctd\u003eCamera input\u003c\/td\u003e\n\u003ctd\u003e2× CSI (mini)\u003c\/td\u003e\n\u003ctd\u003eVia carrier board\u003c\/td\u003e\n\u003ctd\u003eMIPI CSI\u003c\/td\u003e\n\u003ctd\u003e2× 4-lane MIPI CSI-2\u003c\/td\u003e\n\u003ctd class=\"us y\"\u003e1× 4-lane MIPI CSI-2\u003c\/td\u003e\n\u003c\/tr\u003e\n        \u003ctr\u003e\n\u003ctd\u003eVision ISP\u003c\/td\u003e\n\u003ctd\u003eBasic\u003c\/td\u003e\n\u003ctd\u003eYes\u003c\/td\u003e\n\u003ctd\u003eYes\u003c\/td\u003e\n\u003ctd\u003eVPAC + DMPAC\u003c\/td\u003e\n\u003ctd class=\"us y\"\u003eISP + VPAC (WDR · LDC · RGB-IR)\u003c\/td\u003e\n\u003c\/tr\u003e\n        \u003ctr\u003e\n\u003ctd\u003eVideo\u003c\/td\u003e\n\u003ctd\u003e4K60 decode\u003c\/td\u003e\n\u003ctd\u003e4K60\u003c\/td\u003e\n\u003ctd\u003e8K decode\u003c\/td\u003e\n\u003ctd\u003e4K60 encode\/decode\u003c\/td\u003e\n\u003ctd class=\"us\"\u003emicro HDMI out\u003c\/td\u003e\n\u003c\/tr\u003e\n        \u003ctr\u003e\n\u003ctd\u003eExpansion\u003c\/td\u003e\n\u003ctd\u003e40-pin GPIO\u003c\/td\u003e\n\u003ctd\u003e40-pin GPIO\u003c\/td\u003e\n\u003ctd\u003e40-pin GPIO\u003c\/td\u003e\n\u003ctd\u003e40-pin HAT-compatible\u003c\/td\u003e\n\u003ctd class=\"us y\"\u003e40-pin HAT-compatible\u003c\/td\u003e\n\u003c\/tr\u003e\n        \u003ctr\u003e\n\u003ctd\u003eOS\u003c\/td\u003e\n\u003ctd\u003eRaspberry Pi OS\u003c\/td\u003e\n\u003ctd\u003eJetPack (Ubuntu)\u003c\/td\u003e\n\u003ctd\u003eLinux \/ Android\u003c\/td\u003e\n\u003ctd\u003eDebian 13\u003c\/td\u003e\n\u003ctd class=\"us\"\u003eDebian 13 · open SDK\u003c\/td\u003e\n\u003c\/tr\u003e\n        \u003ctr\u003e\n\u003ctd\u003ePower input\u003c\/td\u003e\n\u003ctd\u003eUSB-C 5V\/5A\u003c\/td\u003e\n\u003ctd\u003e7–20V DC\u003c\/td\u003e\n\u003ctd\u003e12V DC\u003c\/td\u003e\n\u003ctd\u003eDC in\u003c\/td\u003e\n\u003ctd class=\"us y\"\u003eUSB-C 5V\/5A · 25W max\u003c\/td\u003e\n\u003c\/tr\u003e\n        \u003ctr\u003e\n\u003ctd\u003eTypical budget\u003c\/td\u003e\n\u003ctd\u003e$ (board only, no AI)\u003c\/td\u003e\n\u003ctd\u003e$$$$\u003c\/td\u003e\n\u003ctd\u003e$$\u003c\/td\u003e\n\u003ctd\u003e$$\u003c\/td\u003e\n\u003ctd class=\"us y\"\u003e$–$$\u003c\/td\u003e\n\u003c\/tr\u003e\n      \u003c\/table\u003e\n    \u003c\/div\u003e\n    \u003cp class=\"fnote\"\u003e* Competitor specifications are collected from public sources for reference only. Please refer to each vendor's official documentation for final data. Need 8 TOPS, dual cameras and 4K60 codec? Step up to the Mo 68A.\u003c\/p\u003e\n  \u003c\/div\u003e\n\n  \u003c!-- Applications --\u003e\n  \u003cdiv class=\"oblock\"\u003e\n    \u003ch3 class=\"otitle\"\u003eApplications\u003c\/h3\u003e\n    \u003cp class=\"odetail\"\u003eFrom a developer's desk to a thousand deployed sites — one compact platform for vision AI at the edge.\u003c\/p\u003e\n    \u003cdiv class=\"apps\"\u003e\n      \u003cfigure\u003e\u003cdiv class=\"ph ph-scene\"\u003e\n\u003cb\u003e图片位 A1 · 场景图 · 900×1200\u003c\/b\u003e智慧工地：安全帽\/人员入侵识别\u003c\/div\u003e\n\u003cfigcaption class=\"cap\"\u003eConstruction Site Safety\u003c\/figcaption\u003e\u003c\/figure\u003e\n      \u003cfigure\u003e\u003cdiv class=\"ph ph-scene\"\u003e\n\u003cb\u003e图片位 A2 · 场景图 · 900×1200\u003c\/b\u003e路侧交通流量分析\u003c\/div\u003e\n\u003cfigcaption class=\"cap\"\u003eTraffic Flow Analysis\u003c\/figcaption\u003e\u003c\/figure\u003e\n      \u003cfigure\u003e\u003cdiv class=\"ph ph-scene\"\u003e\n\u003cb\u003e图片位 A3 · 场景图 · 900×1200\u003c\/b\u003e产线 AI 视觉质检\u003c\/div\u003e\n\u003cfigcaption class=\"cap\"\u003eAI Visual Inspection\u003c\/figcaption\u003e\u003c\/figure\u003e\n      \u003cfigure\u003e\u003cdiv class=\"ph ph-scene\"\u003e\n\u003cb\u003e图片位 A4 · 场景图 · 900×1200\u003c\/b\u003e园区\/周界安防警戒\u003c\/div\u003e\n\u003cfigcaption class=\"cap\"\u003eSecurity \u0026amp; Perimeter\u003c\/figcaption\u003e\u003c\/figure\u003e\n      \u003cfigure\u003e\u003cdiv class=\"ph ph-scene\"\u003e\n\u003cb\u003e图片位 A5 · 场景图 · 900×1200\u003c\/b\u003e零售门店客流分析\u003c\/div\u003e\n\u003cfigcaption class=\"cap\"\u003eSmart Retail Analytics\u003c\/figcaption\u003e\u003c\/figure\u003e\n      \u003cfigure\u003e\u003cdiv class=\"ph ph-scene\"\u003e\n\u003cb\u003e图片位 A6 · 场景图 · 900×1200\u003c\/b\u003e教学\/机器人视觉原型\u003c\/div\u003e\n\u003cfigcaption class=\"cap\"\u003eEducation \u0026amp; Robotics Prototyping\u003c\/figcaption\u003e\u003c\/figure\u003e\n    \u003c\/div\u003e\n  \u003c\/div\u003e\n\n  \u003c!-- Developer Resources · GitHub --\u003e\n  \u003cdiv class=\"oblock\"\u003e\n    \u003ch3 class=\"otitle\"\u003eDeveloper Resources — Open Source on GitHub\u003c\/h3\u003e\n    \u003cp class=\"odetail\"\u003eWe develop in the open. The full Mo 62A developer environment — system images, SDK, AI examples and documentation — is on its way to GitHub, so getting started will be a clone away and every answer stays searchable.\u003c\/p\u003e\n    \u003cdiv class=\"ghub\"\u003e\n      \u003cdiv class=\"gt\"\u003e\n        \u003cb\u003eRepositories launching soon\u003c\/b\u003e\n        \u003cp\u003eSystem images · EdgeAI SDK · TIDL model examples (TFLite \/ ONNX) · GPIO \u0026amp; device-tree docs · Issue tracker — contributions and pull requests will be welcome.\u003c\/p\u003e\n      \u003c\/div\u003e\n      \u003c!-- TODO: 仓库上线后恢复链接 \u003ca class=\"btn\" href=\"正式GitHub链接\"\u003eVisit GitHub →\u003c\/a\u003e --\u003e\n      \u003cspan class=\"btn\" style=\"opacity:.55;cursor:default\"\u003eGitHub — Coming Soon\u003c\/span\u003e\n    \u003c\/div\u003e\n    \u003cdiv class=\"gcards\"\u003e\n      \u003cdiv class=\"gcard\"\u003e\n\u003cb\u003eSystem Images \u0026amp; SDK\u003c\/b\u003e\u003cspan\u003eReady-to-flash Debian images and the open SDK for fully custom system builds.\u003c\/span\u003e\n\u003c\/div\u003e\n      \u003cdiv class=\"gcard\"\u003e\n\u003cb\u003eAI Model Examples\u003c\/b\u003e\u003cspan\u003eEnd-to-end TIDL examples — camera capture, inference and post-processing with OpenCV \/ GStreamer.\u003c\/span\u003e\n\u003c\/div\u003e\n      \u003cdiv class=\"gcard\"\u003e\n\u003cb\u003eDocs \u0026amp; Pinout\u003c\/b\u003e\u003cspan\u003e40-pin header reference, device-tree overlays, camera setup (IMX219) and troubleshooting guides.\u003c\/span\u003e\n\u003c\/div\u003e\n    \u003c\/div\u003e\n    \u003c!-- 图片位 10 · GitHub 截图 --\u003e\n    \u003cdiv class=\"ph ph-wide\" style=\"margin-top:16px\"\u003e\n\u003cb\u003e图片位 10 · GitHub 主页截图 · 1600×900\u003c\/b\u003e建议：GitHub 组织页\/仓库列表截图，展示开源仓库结构\u003c\/div\u003e\n  \u003c\/div\u003e\n\n  \u003c!-- In the box --\u003e\n  \u003cdiv class=\"oblock\"\u003e\n    \u003ch3 class=\"otitle\"\u003eIn the Box\u003c\/h3\u003e\n    \u003c!-- 图片位 09 · 包装清单 --\u003e\n    \u003cdiv class=\"ph ph-wide\"\u003e\n\u003cb\u003e图片位 09 · 包装清单平铺图 · 1600×900\u003c\/b\u003e建议：Mo 62A 主板 + 配件平铺俯拍\u003c\/div\u003e\n    \u003cdiv class=\"pkgs\" style=\"display:grid;grid-template-columns:repeat(3,1fr);gap:16px;margin-top:18px\"\u003e\n      \u003cdiv style=\"background:#fafafa;border:1px solid #e5e5e5;padding:16px 18px;font-size:13px;line-height:1.8\"\u003e\n\u003cb style=\"display:block;color:#1c1b1b;font-size:14.5px;margin-bottom:6px\"\u003eStandard package\u003c\/b\u003eMo 62A board ×1 · Quick start guide ×1\u003c\/div\u003e\n      \u003cdiv style=\"background:#fafafa;border:1px solid #e5e5e5;padding:16px 18px;font-size:13px;line-height:1.8\"\u003e\n\u003cb style=\"display:block;color:#1c1b1b;font-size:14.5px;margin-bottom:6px\"\u003eRecommended accessories\u003c\/b\u003eUSB-C 5V\/5A power supply · Micro SD card · MIPI CSI-2 camera module · Active cooling fan · SBC enclosure · HAT expansion boards\u003c\/div\u003e\n      \u003cdiv style=\"background:#fafafa;border:1px solid #e5e5e5;padding:16px 18px;font-size:13px;line-height:1.8\"\u003e\n\u003cb style=\"display:block;color:#1c1b1b;font-size:14.5px;margin-bottom:6px\"\u003eMemory options\u003c\/b\u003eMo-62A-2G (2GB) · Mo-62A-4G (4GB) · Mo-62A-8G (8GB)\u003c\/div\u003e\n    \u003c\/div\u003e\n  \u003c\/div\u003e\n\n  \u003c!-- Why InHand --\u003e\n  \u003cdiv class=\"oblock\"\u003e\n    \u003ch3 class=\"otitle\"\u003eWhy InHand\u003c\/h3\u003e\n    \u003cp style=\"text-align:center;color:var(--muted);font-size:14px;max-width:980px;margin:0 auto\"\u003eInHand Networks has built industrial connectivity and edge computing devices for over two decades, deployed in energy, transportation, manufacturing and smart-city projects worldwide. The Mo series brings that industrial engineering discipline to edge AI: documented hardware, maintained software, and a supply chain you can plan a product on.\u003c\/p\u003e\n  \u003c\/div\u003e\n\n\u003c\/div\u003e\n\n\u003c!-- ============ TAB 2 · Specifications ============ --\u003e\n\u003cdiv class=\"ocont\"\u003e\n  \u003cp class=\"spectitle\"\u003eHardware\u003c\/p\u003e\n  \u003ctable class=\"spec\"\u003e\n    \u003ctr\u003e\n\u003ctd\u003eProcessor\u003c\/td\u003e\n\u003ctd\u003eTI AM62A74, 4 × Arm Cortex-A53 @ 1.4 GHz\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eAI Accelerator\u003c\/td\u003e\n\u003ctd\u003eC7x DSP + Deep Learning Accelerator (MMA), 2 TOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eISP \/ Vision\u003c\/td\u003e\n\u003ctd\u003eOn-chip ISP + VPAC (RGB-IR, WDR, LDC)\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eRAM\u003c\/td\u003e\n\u003ctd\u003eLPDDR4 4GB (default) \/ 2GB \/ 8GB\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eStorage\u003c\/td\u003e\n\u003ctd\u003eMicro SD card\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eEthernet\u003c\/td\u003e\n\u003ctd\u003e1 × Gigabit Ethernet\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eUSB\u003c\/td\u003e\n\u003ctd\u003e4 × USB 2.0 Type-A\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eDisplay\u003c\/td\u003e\n\u003ctd\u003e1 × micro HDMI\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eCamera\u003c\/td\u003e\n\u003ctd\u003e1 × 4-lane MIPI CSI-2\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eAudio\u003c\/td\u003e\n\u003ctd\u003e3.5 mm jack + PCM (via 40-pin connector)\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eExpansion\u003c\/td\u003e\n\u003ctd\u003e40-pin header: GPIO \/ I²C \/ SPI \/ UART \/ PWM \/ PCM, 3.3 V logic, HAT-compatible\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eDebug\u003c\/td\u003e\n\u003ctd\u003e1 × TTL UART console\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eFan\u003c\/td\u003e\n\u003ctd\u003e1 × 4-pin fan header (active cooling optional)\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eButton\u003c\/td\u003e\n\u003ctd\u003e1 × Reset\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eLED\u003c\/td\u003e\n\u003ctd\u003ePWR, USER\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eRTC\u003c\/td\u003e\n\u003ctd\u003eSupported, with battery backup\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/table\u003e\n\n  \u003cp class=\"spectitle\"\u003eWireless\u003c\/p\u003e\n  \u003ctable class=\"spec\"\u003e\n    \u003ctr\u003e\n\u003ctd\u003eWi-Fi\u003c\/td\u003e\n\u003ctd\u003eWi-Fi 5 (802.11ac), dual-band\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eBluetooth\u003c\/td\u003e\n\u003ctd\u003eBLE 4.2\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eAntenna\u003c\/td\u003e\n\u003ctd\u003eOnboard snap-on antenna (Wi-Fi \/ BLE)\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/table\u003e\n\n  \u003cp class=\"spectitle\"\u003ePower \u0026amp; Mechanical\u003c\/p\u003e\n  \u003ctable class=\"spec\"\u003e\n    \u003ctr\u003e\n\u003ctd\u003ePower Input\u003c\/td\u003e\n\u003ctd\u003eUSB Type-C, 5V \/ 5A DC\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003ePower Consumption\u003c\/td\u003e\n\u003ctd\u003e25 W (max)\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eDimensions\u003c\/td\u003e\n\u003ctd\u003e85 × 56 mm\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eWeight\u003c\/td\u003e\n\u003ctd\u003e47 g\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eOperating Temperature\u003c\/td\u003e\n\u003ctd\u003e0 ~ 50 °C\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eStorage Temperature\u003c\/td\u003e\n\u003ctd\u003e-20 ~ 70 °C\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/table\u003e\n\n  \u003cp class=\"spectitle\"\u003eSoftware\u003c\/p\u003e\n  \u003ctable class=\"spec\"\u003e\n    \u003ctr\u003e\n\u003ctd\u003eOperating System\u003c\/td\u003e\n\u003ctd\u003eDebian 13.2 (Trixie), Linux kernel 6.12\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eAI Runtime\u003c\/td\u003e\n\u003ctd\u003eTI TIDL — deploys TFLite \/ ONNX models\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eVision SDK\u003c\/td\u003e\n\u003ctd\u003eTI EdgeAI SDK; V4L2 camera framework; DRM\/KMS display\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eDevelopment\u003c\/td\u003e\n\u003ctd\u003ePython, C\/C++; OpenCV, GStreamer, NumPy; apt package manager\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eSecurity\u003c\/td\u003e\n\u003ctd\u003eSecure boot, Arm TrustZone, OP-TEE, hardware AES-256\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eOpen SDK\u003c\/td\u003e\n\u003ctd\u003eCustom system builds supported\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eManagement\u003c\/td\u003e\n\u003ctd\u003eSSH remote access; SD card image flashing; UART console\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eNetworking\u003c\/td\u003e\n\u003ctd\u003eTCP\/UDP, ICMP, DNS, DHCP; static routing\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/table\u003e\n\n  \u003cp class=\"spectitle\"\u003eModels\u003c\/p\u003e\n  \u003ctable class=\"spec\"\u003e\n    \u003ctr\u003e\n\u003cth\u003eModel\u003c\/th\u003e\n\u003cth\u003eRAM\u003c\/th\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eMo-62A-2G\u003c\/td\u003e\n\u003ctd\u003e2GB LPDDR4\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eMo-62A-4G\u003c\/td\u003e\n\u003ctd\u003e4GB LPDDR4\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eMo-62A-8G\u003c\/td\u003e\n\u003ctd\u003e8GB LPDDR4\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/table\u003e\n\u003c\/div\u003e\n\n\u003c!-- ============ TAB 3 · Download ============ --\u003e\n\u003cdiv class=\"ocont\"\u003e\n  \u003cdiv class=\"dls\"\u003e\n    \u003cdiv class=\"dl\"\u003e\n      \u003cdiv class=\"ph ph-doc\"\u003e\n\u003cb\u003e图片位 D1 · 封面图 · 800×600\u003c\/b\u003e数据手册封面\u003c\/div\u003e\n      \u003cdiv class=\"db\"\u003e\n\u003cb\u003eMo 62A Datasheet\u003c\/b\u003e\u003cspan\u003ePDF · V1.0 · Full specifications\u003c\/span\u003e\u003cbr\u003e\u003ca class=\"btn\" href=\"#\"\u003eDownload\u003c\/a\u003e\n\u003c\/div\u003e\n    \u003c\/div\u003e\n    \u003cdiv class=\"dl\"\u003e\n      \u003cdiv class=\"ph ph-doc\"\u003e\n\u003cb\u003e图片位 D2 · 封面图 · 800×600\u003c\/b\u003e快速入门封面\u003c\/div\u003e\n      \u003cdiv class=\"db\"\u003e\n\u003cb\u003eQuick Start Guide\u003c\/b\u003e\u003cspan\u003ePDF · First boot \u0026amp; setup\u003c\/span\u003e\u003cbr\u003e\u003ca class=\"btn\" href=\"#\"\u003eDownload\u003c\/a\u003e\n\u003c\/div\u003e\n    \u003c\/div\u003e\n    \u003cdiv class=\"dl\"\u003e\n      \u003cdiv class=\"ph ph-doc\"\u003e\n\u003cb\u003e图片位 D3 · 封面图 · 800×600\u003c\/b\u003eSDK\/系统镜像封面\u003c\/div\u003e\n      \u003cdiv class=\"db\"\u003e\n\u003cb\u003eSDK \u0026amp; System Image\u003c\/b\u003e\u003cspan\u003eDebian image · TIDL · EdgeAI SDK\u003c\/span\u003e\u003cbr\u003e\u003ca class=\"btn\" href=\"#\"\u003eDownload\u003c\/a\u003e\n\u003c\/div\u003e\n    \u003c\/div\u003e\n  \u003c\/div\u003e\n  \u003cdiv class=\"ghub\" style=\"margin-top:22px\"\u003e\n    \u003cdiv class=\"gt\"\u003e\n      \u003cb\u003eMore on GitHub — images, SDK \u0026amp; AI examples\u003c\/b\u003e\n      \u003cp\u003eThe complete developer environment is coming to GitHub: latest system images, SDK source, TIDL model examples and docs. Follow our page for the launch announcement.\u003c\/p\u003e\n    \u003c\/div\u003e\n    \u003c!-- TODO: 仓库上线后恢复链接 \u003ca class=\"btn\" href=\"正式GitHub链接\"\u003eGitHub →\u003c\/a\u003e --\u003e\n    \u003cspan class=\"btn\" style=\"opacity:.55;cursor:default\"\u003eComing Soon\u003c\/span\u003e\n  \u003c\/div\u003e\n\u003c\/div\u003e\n\n\u003c!-- ============ TAB 4 · FAQ ============ --\u003e\n\u003cdiv class=\"ocont\"\u003e\n  \u003cdiv class=\"faq\"\u003e\n\n    \u003cdetails open\u003e\n      \u003csummary\u003eWhat does the Mo 62A offer?\u003c\/summary\u003e\n      \u003cdiv class=\"fa\"\u003e\n        \u003cp\u003eThe Mo 62A is a 2-TOPS AI single-board computer for on-device vision inference at the edge. Key specs:\u003c\/p\u003e\n        \u003ctable\u003e\n          \u003ctr\u003e\n\u003ctd\u003eCompute\u003c\/td\u003e\n\u003ctd\u003e4× Cortex-A53 @ 1.4 GHz + C7x DSP + 2-TOPS deep-learning accelerator\u003c\/td\u003e\n\u003c\/tr\u003e\n          \u003ctr\u003e\n\u003ctd\u003eVision pipeline\u003c\/td\u003e\n\u003ctd\u003eOn-chip ISP + VPAC (RGB-IR, WDR, LDC); 1× 4-lane MIPI CSI-2\u003c\/td\u003e\n\u003c\/tr\u003e\n          \u003ctr\u003e\n\u003ctd\u003eRAM\u003c\/td\u003e\n\u003ctd\u003eLPDDR4 — 2 GB \/ 4 GB (default) \/ 8 GB SKUs (Mo-62A-2G \/ -4G \/ -8G)\u003c\/td\u003e\n\u003c\/tr\u003e\n          \u003ctr\u003e\n\u003ctd\u003eOS\u003c\/td\u003e\n\u003ctd\u003eDebian Linux\u003c\/td\u003e\n\u003c\/tr\u003e\n          \u003ctr\u003e\n\u003ctd\u003eConnectivity\u003c\/td\u003e\n\u003ctd\u003e1× Gigabit Ethernet, Wi-Fi 5 dual-band, BLE 4.2\u003c\/td\u003e\n\u003c\/tr\u003e\n          \u003ctr\u003e\n\u003ctd\u003eOther I\/O\u003c\/td\u003e\n\u003ctd\u003e4× USB 2.0 Type-A, micro HDMI, 3.5 mm audio, 40-pin HAT-compatible header, micro SD\u003c\/td\u003e\n\u003c\/tr\u003e\n          \u003ctr\u003e\n\u003ctd\u003eSecurity\u003c\/td\u003e\n\u003ctd\u003eSecure Boot, Arm TrustZone, OP-TEE, hardware AES-256\u003c\/td\u003e\n\u003c\/tr\u003e\n          \u003ctr\u003e\n\u003ctd\u003ePower \/ size\u003c\/td\u003e\n\u003ctd\u003eUSB Type-C 5 V (25 W max), 85 × 56 mm, 47 g\u003c\/td\u003e\n\u003c\/tr\u003e\n        \u003c\/table\u003e\n        \u003cp\u003eThe 85 × 56 mm board and 40-pin layout are mechanically compatible with standard SBC enclosures and HAT accessories. Typical applications: AI vision boxes, intelligent cameras, defect inspection and other edge-AI inference terminals. The Mo 62A is a developer-oriented SBC managed via SSH and standard Debian tooling — it does not run InHand IEOS or DeviceLive.\u003c\/p\u003e\n      \u003c\/div\u003e\n    \u003c\/details\u003e\n\n    \u003cdetails\u003e\n      \u003csummary\u003eWhich AI frameworks and model formats are supported?\u003c\/summary\u003e\n      \u003cdiv class=\"fa\"\u003e\n        \u003cp\u003eModels run through the TI Deep Learning (TIDL) runtime, which accepts TFLite (\u003cb\u003e.tflite\u003c\/b\u003e) and ONNX (\u003cb\u003e.onnx\u003c\/b\u003e) models. The on-chip ISP\/VPAC handles RAW→RGB conversion, WDR and lens correction so the accelerator gets a clean input; pre- and post-processing in Python or C\/C++ commonly use OpenCV and GStreamer. Ready-to-run model examples are published on our GitHub.\u003c\/p\u003e\n      \u003c\/div\u003e\n    \u003c\/details\u003e\n\n    \u003cdetails\u003e\n      \u003csummary\u003eWhat workloads is 2 TOPS enough for?\u003c\/summary\u003e\n      \u003cdiv class=\"fa\"\u003e\n        \u003cp\u003eAt 2 TOPS the Mo 62A is sized for image classification, lightweight object detection (e.g. YOLO-Nano variants), OCR, presence\/anomaly sensing, and gesture or pose recognition. For multi-stream or high-resolution detection at higher frame rates, step up to the 8-TOPS InHand Mo 68A.\u003c\/p\u003e\n      \u003c\/div\u003e\n    \u003c\/details\u003e\n\n    \u003cdetails\u003e\n      \u003csummary\u003eHow do I use the 40-pin GPIO header?\u003c\/summary\u003e\n      \u003cdiv class=\"fa\"\u003e\n        \u003cp\u003eThe 40-pin HAT-compatible header exposes GPIO, I²C, SPI, UART, PWM and PCM at \u003cb\u003e3.3 V logic\u003c\/b\u003e. Standard SBC HAT accessories mount mechanically — verify electrical compatibility (3.3 V) before connecting.\u003c\/p\u003e\n        \u003cp\u003ePin notes: all user pins default to GPIO, with alternate functions enabled via device-tree overlays; pins 27\/28 are reserved for the camera I²C bus and cannot be used as general GPIO. The board ships with libgpiod v2.x — the \u003cb\u003e-c\u003c\/b\u003e flag is required to specify a chip, and three gpiochips (gpiochip0–gpiochip2) cover the MCU and main domains. Quick examples:\u003c\/p\u003e\n        \u003cpre class=\"code\"\u003egpiodetect                # list chips\ngpioget -c gpiochip2 23   # read pin 11\ngpioset -c gpiochip1 39=1 # drive pin 7 high\ngpiomon -c gpiochip2 23   # watch edges\ni2cdetect -y 2            # scan camera I²C bus\u003c\/pre\u003e\n      \u003c\/div\u003e\n    \u003c\/details\u003e\n\n    \u003cdetails\u003e\n      \u003csummary\u003eHow do I connect cameras, displays and high-bandwidth peripherals?\u003c\/summary\u003e\n      \u003cdiv class=\"fa\"\u003e\n        \u003cp\u003eUse the 4-lane MIPI CSI-2 connector for direct camera input — the IMX219 is supported out of the box via the bundled \u003cb\u003eimx219-preview.sh\u003c\/b\u003e script. The micro HDMI drives local displays, and the four USB 2.0 Type-A ports handle storage, adapters and additional cameras.\u003c\/p\u003e\n      \u003c\/div\u003e\n    \u003c\/details\u003e\n\n    \u003cdetails\u003e\n      \u003csummary\u003eDoes it work with Raspberry Pi HATs and enclosures?\u003c\/summary\u003e\n      \u003cdiv class=\"fa\"\u003e\n        \u003cp\u003eMechanically, yes — the 85 × 56 mm footprint and 40-pin layout match the standard SBC form factor, so mainstream enclosures and HATs mount directly. Electrically, the header is 3.3 V logic: check your HAT's voltage requirements before connecting.\u003c\/p\u003e\n      \u003c\/div\u003e\n    \u003c\/details\u003e\n\n  \u003c\/div\u003e\n\u003c\/div\u003e\n\n\u003c!-- ============ TAB 5 · Reviews ============ --\u003e\n\u003cdiv class=\"ocont\"\u003e\n  \u003c!-- 粘贴你们评价插件（如 Judge.me）的 widget 代码到这里 --\u003e\n  \u003cp style=\"text-align:center;color:#9aa3a8\"\u003eNo reviews yet — be the first to share your Mo 62A project experience.\u003c\/p\u003e\n\u003c\/div\u003e\n\n\u003cscript\u003e\n(function(){\n  var root=document.querySelector('.mo62a');\n  if(!root) return;\n  var tabs=root.querySelectorAll('.otabs li');\n  var conts=root.querySelectorAll('.ocont');\n  tabs.forEach(function(t,i){\n    t.addEventListener('click',function(){\n      tabs.forEach(function(x){x.classList.remove('active')});\n      conts.forEach(function(x){x.classList.remove('active')});\n      t.classList.add('active');\n      if(conts[i]) conts[i].classList.add('active');\n    });\n  });\n})();\n\u003c\/script\u003e\n\u003c\/div\u003e\n","brand":"InHand Networks","offers":[{"title":"2GB","offer_id":49443794911371,"sku":"Mo 62A-2GB","price":169.0,"currency_code":"USD","in_stock":true},{"title":"4GB","offer_id":49443794944139,"sku":"Mo 62A-4GB","price":249.0,"currency_code":"USD","in_stock":true},{"title":"8GB","offer_id":49443794976907,"sku":"Mo 62A-8GB","price":339.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0369\/9536\/7051\/files\/68A-01.png?v=1785318680"},{"product_id":"mo-68a-single-board-computer","title":"Mo 68A Single-Board Computer open-source AI SBC with 8 TOPS for mid-to-high complexity edge inference, including robotics and industrial vision.","description":"\u003ch2 class=\"elementor-heading-title elementor-size-default\"\u003eFlagship open-source AI SBC with 8 TOPS for mid-to-high complexity edge inference, including robotics and industrial vision.\u003c\/h2\u003e","brand":"InHand Networks","offers":[{"title":"4GB","offer_id":50521838780555,"sku":"Mo 68A-4GB","price":329.0,"currency_code":"USD","in_stock":true},{"title":"8GB","offer_id":50521838813323,"sku":"Mo 68A-8GB","price":489.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0369\/9536\/7051\/files\/68A-01_2e4dc4cc-b479-4c41-b6e6-f7a25791ab38.png?v=1785393759"}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0369\/9536\/7051\/collections\/68A-04_bd8f8fd5-082f-4d5e-917d-5662d8b94be0.png?v=1790735805","url":"https:\/\/inhandgo.com\/en-ad\/collections\/ai-single-board-computer.oembed","provider":"InHand Networks","version":"1.0","type":"link"}