Experience with edge and embedded hardware including Raspberry Pi 4, NVIDIA Jetson platforms, depth cameras, FPGA, Hailo AI accelerators, and DSPs. Skilled in evaluating and optimizing their performance for real-time multi-camera computer vision applications that require camera synchronization and medical image processing on edge/IoT systems

OpenCV AI Kit (OAK-D) + OpenVINO

English (rewritten): Combine all previous outputs in this chat into one complete Markdown format table and text. German (rewritten): Kombiniere alle vorherigen Ausgaben in diesem Chat in ein vollständiges Markdown-Format mit Tabelle und Text.

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Comparison of Raspberry Pi 2, Raspberry Pi 4, Raspberry Pi 5, OAK-D, and Jetson Thor 5000T

This document combines all details from earlier answers into one single Markdown format. It includes hardware specs, camera support, AI TOPS, and example YOLO FPS.

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1. Hardware and Camera Support

Model CPU / SoC GPU RAM Native CSI camera ports USB / I/O Networking Power Notes on camera support Raspberry Pi 2 Model B Broadcom BCM2836, Quad-core Cortex-A7 @ 900 MHz VideoCore IV 1 GB 1 × CSI-2 (limited) 4 × USB2 10/100 Ethernet ~5 V via micro-USB Only 1 native camera; USB 2 is bottleneck. Can handle 1 camera at low res (e.g. VGA or 720p low FPS). Raspberry Pi 4 Model B Broadcom BCM2711, Quad-core Cortex-A72 @ 1.5–1.8 GHz VideoCore VI 1–8 GB 1 × CSI-2 (2-lane) 2 × USB3, 2 × USB2 Gigabit Ethernet, Wi-Fi, BT ~5 V, 3 A via USB-C 1 native camera; can add USB/IP cams. Real time possible for 1–2 cams (720p/1080p). Raspberry Pi 5 Broadcom BCM2712, Quad-core Cortex-A76 @ 2.4 GHz VideoCore VII 2–16 GB 2 × CSI/DSI transceivers (4-lane) 2 × USB3, 2 × USB2 GbE, Wi-Fi, BT ~5 V, up to 5 A USB-C 2 native CSI cameras supported. More possible with USB/IP cams. Best Pi option for multi-cam CV. OAK-D (OpenCV AI Kit) Intel Movidius Myriad X VPU (with ISP) Myriad-X NPU On-chip memory + host RAM Stereo depth + RGB camera onboard USB 3.0 to host Host-dependent ~2–5 W Built-in cameras (RGB + stereo). Runs NN on device, offloading Pi/PC. NVIDIA Jetson AGX Thor (T5000) 14-core Arm Neoverse V3AE + Blackwell GPU + Tensor cores Blackwell GPU, 2560 CUDA + NPU Up to 128 GB LPDDR5X, 273 GB/s Up to 16 CSI-2 lanes (≈6 physical cameras + 32 virtual) Multi USB3, PCIe Gen5, NVMe, 25GbE 25GbE + Gigabit 40–130 W Can handle many cameras in real time, both CSI and GMSL. Industrial level.

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1. AI Performance (TOPS / TFLOPS)

Model Dedicated AI accelerator Reported TOPS / perf Raspberry Pi 2 None 0 TOPS Raspberry Pi 4 None 0 TOPS Raspberry Pi 5 None 0 TOPS (external accelerators possible, e.g. Hailo, Coral) OAK-D (Myriad X VPU) Intel Myriad X Neural Compute Engine ~4 TOPS Jetson AGX Thor Blackwell GPU + Tensor cores Up to 2070 FP4 TFLOPS (NVIDIA claim)

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1. Example YOLOv8 Inference FPS (640×640 input, estimated)

Model YOLOv8n (nano/light) YOLOv8s (small) YOLOv8m (mini/medium) Notes Raspberry Pi 2 ~1–2 FPS <1 FPS <0.5 FPS CPU too weak, only low res feasible Raspberry Pi 4 (8 GB) ~5–15 FPS ~2–5 FPS <1–2 FPS Optimized builds (ONNX, TFLite, quantization) improve results Raspberry Pi 5 ~10–30 FPS ~4–10 FPS ~1–4 FPS Best Pi option; still limited without external NPU OAK-D (Myriad X) ~20–60 FPS ~6–15 FPS ~1–4 FPS Runs inference directly on VPU; host load is low Jetson AGX Thor 100–1000+ FPS 30–300 FPS 10–100 FPS Depends on TensorRT optimization, precision (INT8/FP16/FP4)

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1. Short takeaways • Pi 2: Too weak for modern CV, useful only for 1 low-res cam. • Pi 4: Can run 1–2 cameras with lightweight models, but no NPU. • Pi 5: Stronger CPU/GPU, dual CSI ports, still limited by lack of NPU. • OAK-D: Best compact choice if you need built-in depth + AI offload. ~4 TOPS on device. • Jetson Thor: Industrial scale. Many cams, hundreds FPS for YOLO. High power.

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Do you want me to export this as a clean .md file (so you can download and use directly)?

C++

hash

std::map<string,int> prices; prices[‘aa’]=310; prices={‘aa’:310, ‘bb’:410}

stack

std::stack stk; stk.push(5); stk.pop(); //5

queue

std::queue q; q.push(5); ... 89 q.pop(); //5

from collections import deque stk=deque() stk.append(‘asadfas’) stk.pop()

• o(n) stk=[] stk.pop().rstk[-1]

collections import lifo deque() appendLeft(5) pop

stack

• automatically managed
• LIFO
• at compile time
• short term
• 8 MB : 8192 kb
• fast
• ulimit_a : show memory
• push / pop

heap

• need managed by yourself
• if we need to use more than 8 MB we need to use heap
• run time;
• dynamic
• long time
• new/delete
• slower

• pointer

• pmap ‘pidof _____’

  tail _n1

  grep_o ‘[0-9]*’

  awk ‘{print $ 0/(1024*1024)” [GiB]”}’

• tools
  • valgrind ./my_program
  • fsanitize = address
• echo %errorlevel%
• gflags /i print+Greeting.ext +sls

• cdb printGreeting.ext

• start with “/” is absolute path
• start with “folder/file…” is relative path
• ”/” is root
• ”~” is home folder
• ”.” is current folder

• ”..” is parent folder

• [a-c] is abc
• grep & ls.txt search inside file
• ”;” calls all command one after
• && same but if error strop next

• ”

  ” pipe

• htop
• struct like class that all members is public using for simple data

log level

1- debug 2- trace 3- info 4- critical 5- error 6- warning

• basic log
• defined

unit test google test md5sum

git

• git fixes #xx