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| Name | AI on a Pi? Believe it! |
| Video Uploader | Video From Data Slayer |
| Upload Date | This Video Uploaded At 20-01-2024 17:56:54 |
| Video Discription | AI is escalating rapidly... Full tutorial blog post 👉👉 https://www.patreon.com/DataSlayer374/shop/110430 ---------------------------------- Product Links (some are affiliate links) - Raspberry Pi 5 👉 https://amzn.to/3SrbY77 - Coral AI PCIe TPU 👉 https://amzn.to/3U4uROE Pineberry AI Hat https://pineberrypi.com/products/hat-ai-for-raspberry-pi-5 Jeff Geerling https://www.jeffgeerling.com/blog/2023/testing-coral-tpu-accelerator-m2-or-pcie-docker Raspberry Pi 5 https://www.raspberrypi.com/products/raspberry-pi-5/ Coral Edge TPU https://coral.ai/products/m2-accelerator-ae Frigate NVR https://frigate.video/ Discover the Latest Breakthrough in AI Technology with the Pineberry AI Hat for Raspberry Pi 5 In the world of AI and machine learning, the Pineberry AI hat stands as a groundbreaking innovation. This cutting-edge device seamlessly integrates with the Raspberry Pi 5, harnessing the power of the advanced PCI Express bus. This innovative setup notably includes an M2 slot, meticulously designed to accommodate the Coral AI Edge TPU, a compact yet powerful tool in AI technology. The Pineberry AI hat, coupled with the Coral AI Edge TPU, brings unmatched efficiency to the Raspberry Pi platform. Astonishingly, a modestly priced $25 Coral device can outpace a $2,000 CPU in performance. This affordability and power are further enhanced by the capability of the interface to operate at gen 3 speeds, a feature that propels AI capabilities on the Raspberry Pi to unprecedented levels. Our demonstration reveals the remarkable 7ms inference time, showcasing the speed and efficiency of this setup. In our detailed exploration, we utilize the open-source Frigate NVR home surveillance system, accelerated by TPU-enhanced machine learning. Despite Frigate's previous removal of the Raspberry Pi from their recommended hardware list, our configuration achieved faster inference speeds than many other setups. The hardware assembly is straightforward yet sophisticated. It involves mounting the TPU onto the AI hat, securing it with spacers and screws, attaching a 16p FPC ribbon, and finally connecting the AI Hat to an 8GB Raspberry Pi 5. The overall cost for this high-performance setup includes $18.61 for the AI Hat and $24.99 for the Coral AI Chip, totaling an affordable $43.60. Additionally, a USB version is available for $59.99, offering an alternative connection via USB 3.0. The PCIe version of the device boasts advanced thermal management, reducing power draw and inference speed when necessary, ideal for continuous, long-term operation. In contrast, some users find the USB accelerator slightly underpowered, prompting creative solutions within the hobbyist community. The throughput comparison between PCIe gen 3 and USB 3 reveals that while PCIe may offer slightly lower latency, data transfer does not significantly bottleneck these setups. The Raspberry Pi 5's PCIe lane, initially PCIe 2.0 and unofficially upgradable to PCIe 3.0, provides improved performance. For camera integration, we focus on IP cameras and bypass the complexities of RTSP configuration. However, the potential of the Camera Module 3 with its 12 MGP sensor, suitable for HD IoT camera applications, is worth noting. Those with a keen interest in AI will appreciate the ease of installing Google's pycoral library, allowing for the creation and fine-tuning of custom TF lite models. The possibility of utilizing a Dual Edge TPU, doubling resources with minimal additional cost and space, is an exciting prospect. While there are rumors of an official Raspberry Pi M2 hat, currently, the focus seems to be more on NVMe storage solutions. However, the potential of running multiple Edge TPUs on a single installation is a tantalizing thought, especially considering a single TPU can support around ten cameras. In conclusion, while the USB accelerator offers an affordable and efficient alternative, leaving the PCIe slot open for fast storage could significantly enhance the overall performance of the Raspberry Pi system. so8_h0EKPWU |
| Category | Science & Technology |
| Tags | Data Slayer | frigate | raspberry pi 5 | coral ai | pcie | edge tpu | mqtt | pineberry | ai hat | tpu | pcie gen 3 | nvr | home lab | rtsp | ffmpeg | warp terminal | webcam | raspberry pi os | dual edge tpu | coral usb accelerator | mini pcie | tensorflow | libedgetpu1-std | pycoral | python | classify_image.py | inference | /dev/apex_0 | local ai | docker | home assistant | debian | MQTT | OpenHab | NodeRed | ip camera | nvme hat | M.2 card | FPC PCIe ribbon |
