NVIDIA Jetson Nano Developer Kit-B01 Gets an Extra Camera Connector

Jetson Nano Developer Kit B01

Launched in March 2019, NVIDIA Jetson Nano developer kit offered an AI development platform for an affordable $99. The kit is comprised of Jetson Nano module and a carrier board, and the version I received last November ended with “A02”. Jetson Nano developer kit is now getting updated with B01 carrier board that adds an extra MIPI CSI connector and other few changes, including compatibility with NVIDIA Jetson Nano production module (with eMMC flash instead of MicroSD card). Jetson Nano developer kit-B01 specifications: B01 Jetson Nano CPU Module 128-core Maxwell GPU Quad-core Arm A57 processor @ 1.43 GHz System Memory  – 4GB 64-bit LPDDR4 @ 25.6 GB/s Storage  – microSD card slot Video Encode – 4K @ 30 | 4x 1080p @ 30 | 9x 720p @ 30 (H.264/H.265) Video Decode – 4K @ 60 | 2x 4K @ 30 | 8x 1080p @ 30 | 18x 720p @ 30 (H.264/H.265) Dimensions – 70 x 45 mm B01 Baseboard 260-pin …

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MediaTek Rich IoT SDK v20.0 Released, Pumpkin i500 SBC Announced

MediaTek i500 AI Vision Platform

MediaTek Rich IoT SDK v20.0 is Available MediaTek has announced its Rich IoT SDK v20.0  is already available for the i300 and i500 chipset series. The SDK was developed in collaboration with BayLibre, the French developer of Linux and Android embedded systems software, which is also known for helping mainlining Amlogic processors to Linux. The i300A, i300B, and i500 hardware platforms are supported and the features are focused on IoT and the emerging generation of smart devices. Supported OS’es and Test Applications The Rich IoT SDK v20.0 supports Yocto 3.0 Linux and Android 10 to let third-party customers and members of the MediaTek Ecosystem test Computer Vision algorithms, AI models and custom software on top of the base layer. Updates and Maintenance The SDK is receiving updates quarterly, with security updates and patches being delivered over-the-air (OTA) on a regular basis. The chipset series has a timeline for updates to features and security for device manufacturers to leverage the latest …

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SolidRun Janux GS31 Edge AI Server Combines NXP LX2160A & i.MX 8M SoCs with 128 Gyrfalcon AI Accelerators

SolidRun Janux GS31-Edge AI Inference Server

AI inference used to happen exclusively in powerful servers hosted in the cloud, but in recent years great efforts have been made to move inference at the edge, usually meaning on-device, due to much lower latency, and improved privacy. On-device inference works, but obviously, performance is limited, and on battery-operated devices, one also has to consider power consumption. So for some applications, it makes sense to have a local server with much more processing power than devices, and lower latency than the cloud. That’s exactly the use case SolidRun Janux GS31 Edge AI inference server is trying to target using several NXP processors combined with up to 128 Gyrfalcon Lightspeeur SPR2803 AI accelerators Janux GS31 server specifications: CPU Module – CEx7 LX2160A COM Express module with NXP LX2160A 16-core Arm Cortex A72 processor @ 2.0 GHz System Memory – Up to 64GB DDR4 RAM via 2x SO-DIMM sockets “Video” Processors – Up to 32x NXP i.MX 8M Cortex-A53 SoC with …

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Getting Started with Amlogic NPU on Khadas VIM3/VIM3L

output type 2 yolov3

Shenzhen Wesion released the NPU toolkit for Khadas VIM3/VIM3L last November, so I decided to try the latest Ubuntu 18.04 image and the NPU toolkit on Khadas VIM3L, before switching to VIM3 for reasons I’ll explain below. I’ve followed two tutorials from the forum and wiki to run pre-built samples and then building a firmware image and samples from source. Khadas VIM3L and VIM3 Have Different & Optional NPUs This will be obvious to anyone who read the specs for Khadas VIM3 and VIM3L that the former comes with a 5 TOPS NPU, while the one in the latter only delivers up to 1.2 TOPS. But somehow, I forgot about this, and assume both had the same NPU making VIM3L more attractive but this type of task, Obviously I was wrong. But the real reason I stopped using Khadas VIM3L can be seen in the photo below. My board is an early sample that comes with Amlogic S905D3 processor, but …

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Lattice Introduces CrossLink-NX FPGA for Edge AI & Embedded Vision

Lattice CrossLink-NX FPGA Lattice Semiconductor has announced the first product associated with its Nexus Platform, the CrossLink-NX FPGA designed for embedded vision and Edge AI applications. There are two offerings at this time, the CrossLink-NX FPGA 17, and the CrossLink-NX FPGA 40. Recent Announcements The Nexus Platform was introduced at the beginning of December 2019, and now CrossLink-NX has been developed and is being manufactured. The first announcements of Lattice Nexus Platform and The CrossLink-NX  Product Family came as the company’s moved to capture the embedded vision systems market. The Standout Features The low-power consumption, low soft error immunity, and 10Gbps MIPI are highlights of the CrossLink-NX FPGA. Other features include Instant On, with IO configured in 3 ms, and a total of 8 ms for the device. The Cross-Platform FPGAs The trends in technology are leading to devices that can cross function in a number of different tech environments. Nexus Platform systems are offered to support such applications as …

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Raspberry Pi 4 Powered Open Source Hardware Robot Paves the Way for Robot Maids

Raspberry Pi 4 Open Source Hardware Robot

Eventually, we all expect robots to do chores and other manual tasks performed by humans such as preparing and serving food at restaurants, carry objects over rough terrain as opposed to just inside the warehouse with a flat floor, or even moves pieces on a chessboard when other humans are no here to play with us. I’m fully expecting to eventually own a robot maid of sorts to wash dishes, mop the floors, and perform other tasks on my behalf. We are not there yet, but Raspberry Pi 4 powered Pollen Robotics’ Reachy open source-hardware robot is getting us closer to the goal as it can handle small objects and via two robotic arms and a dual-camera head, and can also interact with humans using a microphone and a speaker. Key features and specifications of Reachy robot: Main body SBC – Raspberry Pi 4 SBC with 2GB according to a teardown on Tom’s hardware AI accelerator – Google Coral AI …

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NXP i.MX 8M Plus Processor Targets AI Applications with a 2.3 TOPS Neural Processing Unit

NXP i.MX 8M Plus

NXP has just announced its first i.MX processor with a dedicated neural processing unit (NPU) at CES 2020. The NXP i.MX 8M Plus SoC is built upon the existing i.MX 8M Nano family with a quad-core Arm Cortex-A53 processor running at up to 2GHz, an independent real-time Cortex-M7 microcontroller @ 800MHz, and a Vivante 3D GPU, but adds a 2.3 TOPS NPU to the mix. The NPU will enable advanced machine learning inference at the industrial and IoT (Internet-of-Things) edges for applications such as people and object recognition for public safety, industrial machine vision, robotics, hand gesture, and emotion detection with natural language processing. NXP i.MX 8M Plus key features and specifications: CPU – Quad-core Arm Cortex-A53 processor @ up to 2.0 GHz with 512KB ECC cache Real-time MCU – Arm Cortex-M7 @ up to 800 MHz GPU – Vivante GC7000UL 3D GPU, Vivante GC520L 2D GPU DSP – HiFi 4 DSP for voice and natural language processing AI Accelerator …

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Google Coral mPCIe and M.2 Cards for Sale, New Coral Dev Board Mini and Modules Coming in 2020

Google introduced Coral development board and USB accelerator with Google Edge TPU last year. The development board was comprised of a baseboard and Coral system-on-module with an NXP i.MX 8M quad-core Arm Cortex-A53 processor and the Edge TPU. Since then ASUS announced Tinker Edge T and CR1S-CM-A SBC based on the Coral module, and yesterday, I noticed Seeed Studio started selling mPCIe and M.2 AI accelerator card with Google Edge TPU, while today, Google announced upcoming Coral products for 2020. Coral Mini PCIe and M.2 Accelerators Coral Mini PCIe card specifications: Half-mini PCIe card with PCIe Gen2 x1 Supply voltage –  3.3VDC +/- 10 % Dimensions – 30.00 x 26.80 x 2.55 mm Weight – 3.6 g Temperature Range – Storage: -40 ~ 85°C; operating: -20 ~ 70°C Relative humidity – 0 ~ 100% (non-condensing) Op-shock – 100 G, 11ms (persistent); 1000 G, 0.5 ms (stress); 1000 G, 1.0 ms (stress) Op-vibe (random) – 0.5 Grms, 5 – 500 Hz …

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