96Boards RK1808 & RK3399Pro SoM & Devkit Now Available for Purchase

RK3399Pro SoM Development Kit

Back in April, we covered the very first 96Boards SoM’s (Systems-on-Module) which were based on Rockchip RK3399Pro or RK1808 processors, and targeted applications leveraging artificial intelligence acceleration. There were not quite available at the time, but Seeed Studio now has both BeiQi modules for pre-order for $119 and $59 respectively, while the carrier board goes with $125 with antennas, and power supply. Note that the RK3399Pro SoM and the carrier board are basically available now with shipping schedule for July 4th, but you’d had to wait until the end of the month for the RK1808 module. BeiQi RK1808 AIoT 96Boards Compute SoM Module specifications: SoC – Rockchip RK1808 dual-core Arm Cortex-A35  processor @ 1.6 GHz with NPU supporting 8-bit/16-bit operations up to 3.0 TOPS, TensorFlow and Caffe frameworks; 22nm FD-SOI process System Memory – 1GB LPDDR3 (I also read “4GB LPDRR3” (sic.) in other places, but the capacity is likely wrong) Storage – 16GB eMMC flash Networking – Gigabit Ethernet …

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AAEON AI Core XP4/XP8 PCIe Card Combines up to 8 Myriad X VPU’s

AAEON AI Core XP4 XP8

Movidius Myriad X is Intel’s latest vision processing unit (VPU) first unveiled in 2017, and available for evaluation in Intel Neural Compute Stick 2 since the end of 2018. Later on, AAEON also launched their own AI Core XM2280 M.2 card equipped with two Myriad X 2485 VPU’s and capable of up to 200 fps (160 fps typical) inferences, thanks to over 2 TOPS of deep neural network (DNN) performance. But what if you need even more performance? The company has now launched AI Core XP4/XP8 card with either two or four AI Core XM2280 M.2 cards that can be connected into any computer or workstation with a PCIe x4 slot. AAEON AI Core XP4/XP8 specifications: 4x M.2 sockets for 2x or 4x M.2 2280 M-key cards with 2x Myriad X VPU’s and 2x 4Gbit LPDDR4x memory each Asmedia PCIe switch Cooling – Fan heatsink PCIe x4 standard full-length low profile slot card Dimensions – 167 x 111 mm Temperature …

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$69.99 Gyrfalcon 2803 Plai Plug Delivers 24 TOPS per Watt

2803 Plai Plug

Last year we covered an alternative to Intel Movidius Neural Compute Stick with Orange Pi AI Stick 2801 powered by Gyrfalcon Lightspeeur 2801S neural processor, and delivering up to 5.6 TOPS, or 2.8TOPS @ 300mW for $69.  Since then Gyrfalcon introduced Lightspeeur 2803(S) AI accelerator delivering up to 24 TOPS, or 16.8 TOPS @ 700 mW. We’ve recently seen the new neural processor will be integrated into SolidRun  i.MX 8M Mini & Nano systems-on-module, and today the company published a press release to announce their latest 2803 Plai Plug providing an upgrade to their existing 2801 Plai Plug (Orange Pi AI Stick 2801) for about the same $69.99 price tag. Gyrfalcon 2803 Plai Plug preliminary specifications: AI Accelerator – Gyrfalcon Lightspeeur 2803S with 2-dimensional Matrix Processing Engine (MPE) and AI Processing in Memory (APiM) Storage – eMMC flash Host interface – USB 3.0 port Power Consumption – 700mW at 16.8 TOPS (24 TOPS per watt) Dimensions – 66.5 x 20.5 …

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AI Core XM2280 M.2 Card is Equipped with two Myriad X 2485 VPUs

AI Core XM2280

AAEON released UP AI Core mPCIe card with a Myriad 2 VPU (Vision Processing Unit) last year. But the company also has an AI Core X family powered by the more powerful Myriad X VPU with the latest member being AI Core XM2280 M.2 card featuring not one, but two Myriad X 2485 VPUs coupled with 1GB LPDDR4 RAM (512MB x2). The card supports Intel OpenVINO toolkit v4 or greater, and is compatible with Tensorflow and Caffe AI frameworks. AI Core XM2280 M.2 specifications: VPU – 2x Intel Movidius Myriad X VPU, MA2485 System Memory – 2x 4Gbit LPDDR4 Host Interface – M.2 connector Dimensions – 80 x 22 mm (M.2 M+B key form factor) Certification – CE/FCC Class A Operating Temperature – 0~50°C Operating Humidity – 10%~80%RH, non-condensing The card works with Intel Vision Accelerator Design SW SDK available for Ubuntu 16.04, and Windows 10. Thanks to the two Myriad X VPU’s, the card is capable of up to …

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96Boards AI Sophon Edge Developer Board Features Bitmain BM1880 ASIC SoC

96boards Sophon Edge

Bitmain, a company specializing in cryptocurrency, blockchain, and artificial intelligence (AI) application, has just joined Linaro, and announced the first 96Boards AI platform featuring an ASIC: Sophon BM1880 Edge Development Board, often just referred to as “Sophon Edge”. The board conforms to the 96Boards CE specification, and include two Arm Cortex-A53 cores, a Bitmain Sophon Edge TPU delivering 1 TOPS performance on 8-bit integer operations, USB 3.0 and gigabit Ethernet. Sophon Edge specifications: SoC ASIC – Sophon BM1880 dual core Cortex-A53 processor @ 1.5 GHz, single core RISC-V processor @ 1 GHz, 2MB on-chip RAM, and a TPU (Tensor Processing Unit) that can provide 1TOPS for INT8,and up to 2 TOPs by enabling Winograd convolution acceleration System Memory – 1GB LPDDR4 @ 3200Mhz Storage – 8GB eMMC flash + micro SD card slot Video Processing – H.264 decoder, MJPEG encoder/decoder, 1x 1080p @ 60fps or 2x 1080p @ 30fps H.264 decoder, 75fps for FHD images Connectivity – Gigabit Ethernet(RJ-45), Wifi, Bluetooth …

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$70 UP AI Core mini PCIe Card Features Intel Movidius Myriad 2, Supports Tensorflow and Caffe Frameworks

AAEON’s Up Board has given us some affordable Intel development boards over the years with products such as the Cherry Trail based UP Board, or Apollo Lake powered UP Squared board among others, that are competitively priced against equivalent Arm development boards. The company has now launched a new UP AI Edge family, which will include hardware based on Intel Altera FPGA or Intel Movidius VPU (Vision Processing Unit). Their first product is based on the latter, more exactly Movidius 2 2450 VPU, and instead of being a standalone board, UP AI Core is a mini PCIe card that can fit into any 64-bit Intel board or computer. UP AI Core card specifications: SoC – Intel Movidius Myriad 2 2450 VPU System Memory – 512MB DDR SDRAM Mini PCIe edge connector Dimensions – 51 x 30 mm Host computer/board requirements System Memory – 1GB RAM or more Storage – 4GB of free storage Free mini PCIe slot x86_64 computer running …

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Arm’s Project Trillium Combines Machine Learning and Object Detection Processors with Neural Network Software

We’ve already seen Neural Processing Units (NPU) added to Arm processors such as Huawei Kirin 970 or Rockchip RK3399Pro in order to handle the tasks required by machine learning & artificial intelligence in a faster or more power efficient way. Arm has now announced their Project Trillium offering two A.I. processors, with one ML (Machine Learning) processor and one OD (Object Detection) processor, as well as open source Arm NN (Neural Network) software to leverage the ML processor, as well as Arm CPUs and GPUs. Arm ML processor key features and performance: Fixed function engine for the best performance & efficiency for current solutions Programmable layer engine for futureproofing the design Tuned for advance geometry implementations. On-board memory to reduce external memory traffic. Performance / Efficiency – 4.6 TOP/s with an efficiency of 3 TOPs/W for mobile devices and smart IP cameras Scalable design usable for lower requirements IoT (20 GOPS) and Mobile (2 to 5 TOPS) applications up to …

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Rockchip RK3399Pro SoC Integrates a 2.4 TOPS Neural Network Processing Unit for Artificial Intelligence Applications

Rockchip RK3399 (aka OP1) SoC was launched in 2016 with an hexa core Arm Cortex A72/A53 processor, Mali-T860MP4 GPU, support for 4K video decoding, and high speed interfaces like USB 3.0 and PCIe, as well as Gigabit Ethernet. The processor is found in Chromebooks, TV boxes, development boards, and other devices. The company has unveiled an upgraded “Pro” version of the processor at CES 2018. Rockchip RK3399Pro appears to have most of the same features as its predecessor but adds a neural network processing unit (NPU) delivering up to 2.4 TOPS for artificial intelligence and deep learning applications. The company claims that compared to traditional solution, the computing performance of typical deep neural network Inception V3, ResNet34 and VGG16 models on RK3399Pro is improved by almost one hundred times, and power consumption is less than 1% than A.I. solutions implemented using GPU acceleration. Based on the information provided in the chart above (source: elDEE on twitter), Rockchip RK3399Pro outperforms other …

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