J2021 is a hand-size edge AI box built with Jetson Xavier NX 8GB module which delivers up to 21 TOPs AI performance, rich set of IOs including USB 3.1 ports(4x), M.2 key E for WIFI, M.2 Key M for SSD, RTC, CAN, Raspberry Pi GPIO 40-pin, and so on, aluminum case, cooling fan, pre-installed JetPack System, as NVIDIA Jetson Xavier NX Dev Kit alternative, ready for your next AI application development and deployment The full system includes the same form factor carrier board as the Jetson NX Developer Kit, one Jetson Xavier NX production module, a heatsink, and a power adapter. New Arrival: If you are looking for a full system that comes with industrial communications such as RS232/RS485. Please check our new industrial A203 and A205- E mini PCs, with pre-installed Jetpack 5.0.2, 128GB SSD, and WIFI/BT module, as also industrial interfaces. You can train a brand new custom model just in hours, or you can even use choose one of 130 pre-trained models from the always dashboard, deploy it to edge devices, and build a computer vision application within minutes! With an extensive library of Python APIs, you can also customize any AI application, and also push real-time analytics to platforms such as Data Lakes and BI tools for further data visualization.

This partnership delivers an AI solution that accelerates the deployment of computer vision applications on Seeed's edge devices by integrating the alwaysAI computer vision platform. Developers and enterprises are dealing with unreasonable computer vision timelines and difficulty in deploying production applications to IoT devices. This revolutionary new approach will help millions of developers and their companies create computer vision applications that'll work seamlessly on their IoT devices, such as Seeed Studio's reComputer of Jetson series and Odyssey X86. Developers can add the alwaysAI runtime engine and deployment capabilities when purchasing their IoT devices to deploy their computer vision solutions faster than ever. "Accelerating deployment of computer vision applications on IoT devices will set developers and companies up to be able to scale their CV applications much faster," said Steve Griset, CTO & Co-Founder of alwaysAI.

I had seen the Edge Impulse development platform for machine learning on edge devices being used by several boards, but I hadn't had an opportunity to try it out so far. So when Seeed Studio asked me whether I'd be interested to test the nRF52840-powered XIAO BLE Sense board, I thought it might be a good idea to review it with Edge Impulse as I had seen a motion/gesture recognition demo on the board. It was quite a challenge as it took me four months to complete the review from the time Seeed Studio first contacted me, mostly due to poor communications from DHL causing the first boards to go to customs' heaven, then wasting time with some of the worse instructions I had seen in a long time (now fixed), and other reviews getting in the way. But I finally managed to get it working (sort of), so let's have a look. Since the gesture recognition demo used an OLED display, I also asked for it and I received the XIAO BLE board (without sensor), the XIAO BLE Sense board, and the Grove OLED Display 0.66″.

Each successive generation of Raspberry Pi has brought something new to the table. The latest release, the Raspberry Pi 4, is no exception, upgrading the low-cost single-board computer to include true gigabit Ethernet connectivity, a high-performance 64-bit central processor, more powerful graphics processor, and up to 4GB of RAM. It's a low-cost way to play with RISC-V and Kendryte's KPU, but more expensive than an Arduino for microcontroller use and too limited for general-purpose AI work. Even with these impressive-for-the-price specifications, though, there's something the Raspberry Pi can't easily do unaided: deep learning and other artificial intelligence workloads. With an explosion of interest in AI-at-the-edge, though, there's a market for Raspberry Pi add-ons which offer to fill in the gap - and the Grove AI HAT is just such a device, billed by creator Seeed Studio as ideal for AI projects in fields from hobbyist robotics to the medical industry.

Over the last six months I've been looking at deep learning on the edge, and investigating the new generation of custom silicon designed to speed up machine learning inferencing on embedded devices. The original accelerator hardware was launched by Intel back in 2017, but since then we've seen more hardware from Intel, Google, NVIDIA, and others. Right now, we're seeing a deluge of new hardware based around the Intel Movidius, and the Gyrfalcon Lightspeeur chips. I'm also expecting to see hardware based around Google's Edge TPU later in the year when their System-on-Module (SoM) is finally available in volume. However, there are other less known players in the accelerator market, one of these is Rockchip with their Neural Processing Unit (NPU).

Last year we wrote about Kendryte K210 dual core RISC-V processor specifically designed for for machine vision and machine hearing as well as the corresponding Kendryte KD233 which enables inference at the edge, e.g. Latter on we found the processor in Sipeed M1 module which went for as low as $5 in a crowdfunding campaign, and was fitted to some low cost boards now selling for $12.90 on Seeed Studio. The latter company has now designed Grove AI HAT that aims to assist Raspberry Pi in running the edge computing workloads previously described, as exposes 6 Grove interfaces to extend functionality with some of the Grove add-on modules. The board can either be used in conjunction with a Raspberry Pi board, or in standalone via a USB-C cable. It supports the baremetal Kendryte Standalone SDK, as well as ArduinoCore K210 for the Arduino IDE working in Linux, Windows, and Mac OS meaning it's possible to leverage the Grove Arduino libraries and other Arduino libraries on this board.