More Details about Rockchip RK3399Pro SoC, and RK1808 NPU

RK3399Pro Block Diagram

First announced in January 2018, Rockchip RK3399Pro was supposed to be a pin-to-pin compatible with Rockchip RK3399 processor, and adding a Neural-Network Processing Unit (NPU) capable of delivering 2.4 TOPS for acceleration A.I. workloads. Shortly after Pine64 announced they’d be offering Rockpro64-AI board in August, and later on Vamrs unveils ROCK960 PRO at a Linaro Connect event with an expected Q2 2018 launch. But none of the RK3399Pro boards are available, as there have been delays with RK3399Pro, and some commented an external NPU would be launched first with further details.   But today – courtesy of Vamrs – we have some more details about RK3399Pro features, a likely explanation for the delay, and some information about Rockchip RK1808 NPU chip. Contrary to the CES 2018 announcement, Rockchip RK3399Pro will come in a 27x27mm FCBGA1372 package instead of the 22x22mm FCBGA828 package for RK3399. So pin-to-pin compatibility is out of the windows, and boards will have to be designed specifically …

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HiSilicon Unveils Kirin 980 Octa-core Cortex A76/A55 Processor with Mali-G76 GPU

Arm announced Cortex A76 CPU core and Mali-G76 GPU in June of this year, and in the past, we normally had to wait about a year between the announcement and the actual launch of SoC with the new IP core. But courtesy of HiSilicon (Huawei), we already know of one Cortex A76 processor with Kirin 980 processor featuring three clusters with two high performance Cortex A76 cores, two Cortex A76 cores clocked at a low frequency, and four Cortex A55 efficiency core. The company also added the new Mali-G76 GPU for good measure. Kirin 980 specifications and key features: CPU (DynamIQ clusters) 2x Cortex A76 cores @ up to 2.60 GHz with 512KB L2 cache 2x Cortex A76 cores @ up to 1.92 GHz with 512KB L2 cache 4x Cortex A55 cores  @ 1.80 GHz with 128KB L2 cache 4MB shared L3 cache GPU – ARM Mali-G76MP10 @ 720 MHz NPU – Dual NPU (Neural Processing Unit) with twice the …

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Lindenis V5 Allwinner V5 SBC is Designed for AI Video Processing, 4K Encoding

Allwinner V5 SBC

Allwinner V5 V100 is a new quad core Cortex A7 processor targeting 4K 30 fps (Linux)  cameras, and integrating AIE intelligent analytic acceleration engine handling motion detection, perimeter defense video diagnosis, and face detection. Usually, it’s pretty hard to get a development board based on a new processor, but Lindenis V5 single board computer based on the processor is already available in China, and comes with 1 to 2GB RAM, HDMI 1.4 and MIPI DSI video outputs, dual MIPI CSI video outputs, Gigabit Ethernet and more. Lindevis V5 SBC specifications: SoC – Allwinner V5 Quad core Arm Cortex-A7 processor @ up to 1,512 MHz with NEON, VFPv4 FPU 4K @ 30 fps H.265/H.264 encoder and decoder Dual ISP [email protected] + [email protected] AIE (AI Engine) Architecture – Built-in with intelligent analytics acceleration engine with support for motion detection, perimeter defense, video diagnosis, face detection, flow statistics. Supports binocular depth map. System Memory – 1 or 2GB RAM Storage – Micro SD …

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Embedded Linux Conference Europe & OpenIoT Summit Europe 2018 Schedule

Embedded Linux Conference OpenIOT Summit Europe 2018

The Embedded Linux Conference & OpenIoT Summit 2018 took place in March of this year in the US, but the European version of the events are now planned to take place on October 21-24 in Edinburg, UK, and the schedule has already been released. So let’s make a virtual schedule to find out more about some of interesting subjects that are covered at the conferences. The conference and summit really only officially start on Monday 22, but there are a few talks on Sunday afternoon too. Sunday, October 21 13:30 – 15:15 – Tutorial: Introduction to Quantum Computing Using Qiskit – Ali Javadi-Abhari, IBM Qiskit is a comprehensive open-source tool for quantum computation. From simple demonstrations of quantum mechanical effects to complicated algorithms for solving problems in AI and chemistry, Qiskit allows users to build and run programs on quantum computers of today. Qiskit is built with modularity and extensibility in mind. This means it is easy to extend its …

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Snapdragon 670 Launched with Better Performance and Camera Capabilities, Artificial Intelligence Tech

Snapdragon 670

Qualcomm has just introduced another of their “Mobile Platforms” with Snapdragon 670. The chipset comes with eight Kryo 360 cores, a Snapdragon X12 LTE modem for download speeds up to 600 Mbps,  as well as Qualcomm AI engine and  Hexagon 685 DSP for accelerating artificial intelligence workloads. The Qualcomm Spectra 250 ISP found in the SoC is said to enable most of the premium camera features found in professional cameras including noise reduction, image stabilization, and active depth sensing. Snapdragon 670 specifications: CPU – Octa-core processor with a cluster of 2x Kryo 360 performance cores @ up to 2.0 GHz, and a cluster of 6x Kryo 360 efficiency cores @ up to 1.7 GHz Visual Processing System (aka GPU) – Adreno 615 GPU with support for Open GL ES 3.2, Open CL 2.0, Vulkan, DirectX 12, H.264/H.265/VP9 Ultra HD video playback, DisplayPort over USB Type-C DSP – Hexagon 685 DSP with 3rd Gen Vector Extensions, Qualcomm All-Ways Aware Sensor Hub, …

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Amlogic A311D Media Processor Features Cortex A73/A53 Cores, an Arm Mali-G52 MP4 GPU

As far as processors are concerned, it’s been pretty quiet in the TV box space in this year with for example, devices based on Amlogic S922 yet to launch, and new TV boxes are normally launched with processors such as Amlogic S905X/Z/W or S912, as well as Rockchip RK3328 or RK3229. But this morning,  I’ve been informed that Amlogic is working on a more powerful media processor based on info from buildroot release notes  Amlogic A311D is an hexa-core processor featuring four Arm Cortex A73 cores, two Cortex A53 cores, and an Arm Mali-G52-MP4 GPU. Amlogic A311D specifications known so far: CPU – 4x Arm Cortex A73 @ TBD GHz, 2x Arm Cortex A53 GPU – Arm Mali-G52 MP4 Memory I/F – DDR3/4; LPDDR3/4 Storage I/F – eMMC, SD/SDHC/SDXC Video Output – HDMI Tx up to 4K2K @ 60 Hz with CEC & HDR support, CVBS Video Processing Unit (VPU) Decoding – 4K (up to 4096x 2304) H.265, VP9, and …

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GAPUINO GAP8 is a $229 RISC-V MCU Developer Kit for A.I. Applications

GAPuino Board

GreenWaves GAP8 is a low power RISC-V “MCU class” processor with eight compute cores optimized for artificial intelligence applications, and its main selling point is the ability to do tasks like computer vision or audio processing at very low power, even good enough to run on batteries. When we first covered GAP8 RISC-V processor at the beginning of the year, the company also mentioned a development kit comprised of GAPDUINO Arduino compatible board, a sensor board, and a QVGA camera module to experiment with the solution.  The board and development kit are now easier to purchase as the devkit is sold on SeeedStudio for $229. GAPuino board specifications: SoC – GAP8 IoT Application Processor with 8x RISC-V  compute cores, 1x RISC-V fabric controller core delivering up to 200 MOPS at 1mW and  >8 GOPS at a few tens of mW Memory / Storage –  HyperBus combo DRAM/Flash with 512 Mbit Flash + 64 Mbit DRAM; 256 Mbit Quad SPI flash …

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Google Unveils Edge TPU Low Power Machine Learning Chip, AIY Edge TPU Development Board and Accelerator

AIY Edge TPU Dev Board

Google introduced artificial intelligence and machine learning concepts to hundreds of thousands of people with their AIY projects kit such as the AIY Voice Kit with voice recognition and the AIY Vision Kit for computer vision applications. The company has now gone further by unveiling Edge TPU, its own  purpose-built ASIC chip designed to run TensorFlow Lite ML models at the edge, as well as corresponding AIY Edge TPU development board, and AIY Edge TPU accelerator USB stick to add to any USB compatible hardware. Google Edge TPU (Tensor Processing Unit) & Cloud IoT Edge Software Edge TPU is a tiny chip for machine learning (ML) optimized for performance-per-watt and performance-per-dollar.  It can either accelerate ML inferencing on device, or can pair with Google Cloud to create a full cloud-to-edge ML stack. In either case, local processing reduces latency, remove the needs for a persistent network connection, increases privacy, and allows for higher performance using less power. The chip will …

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