intel News - Page 20 of 45 - CNX Software

August 14, 2017August 15, 2017 by Jean-Luc Aufranc (CNXSoft) - 6 Comments

Intel Gemini Lake Block Diagram and Yet More Info

So yesterday, I wrote about some of the new features of Intel Gemini Lake processors like native HDMI 2.0, 4-wide pipeline, 10-bit VP9, and possible built-in 802.11ac wireless controller. I went to bed, and somehow this morning I woke up with something that looks like Gemini Lake (GLK) block diagram, and a few more details. So we indeed have HDMI 2.0 output, as well as DP 1.2a and eDP 1.4, and an embedded wireless controller via the CNVi (Connectivity Integration Architecture) block for WiFi’s MAC and Bluetooth’s MAC + Baseband modem. We’ll have plenty of USB 3.0 host interfaces, and the usual PCIe and SATA 3 interfaces. Still no UFS support, but eMMC 5.1 is supported, as well as x128 DDR4, LPDDR3 and LPDDR4 memory up to 2400 MHz (No ECC support). Cache size is confirmed to be 4MB for up to four GoldMont Plus (GLM+) cores, which combined with […]

August 13, 2017August 13, 2017 by Jean-Luc Aufranc (CNXSoft) - 6 Comments

New Features of Intel Gemini Lake Processors – HDMI 2.0, 10-bit VP9 Codec, 4-Wide Pipeline, and More

Most recent low power Intel mini PCs are now based on Apollo Lake family with SoC such as Celeron N3450 or Pentium N4200, but we’ve known for a while that Gemini Lake processors will succeed those starting in Q4 2017, and we can expect some Celeron/Pentium SKUs like Intel Pentium J5005 or Intel Celeron N4000, but so far I had not seen that many details. However, an anonymous tip pointed me to some interesting publicly available information. First, a kernel patch reveals a little about the CPU pipeline: Add perf core PMU support for Intel Goldmont Plus CPU cores: – The init code is based on Goldmont. – There is a new cache event list, based on the Goldmont cache event list. – All four general-purpose performance counters support PEBS. – The first general-purpose performance counter is for reduced skid PEBS mechanism. Using :ppp to indicate the event which want […]

August 9, 2017August 9, 2017 by Jean-Luc Aufranc (CNXSoft) - 16 Comments

Movidius Neural Compute Stick Shown to Boost Deep Learning Performance by about 3 Times on Raspberry Pi 3 Board

Intel recently launched Movidius Neural Compute Stick (MvNCS)for low power USB based deep learning applications such as object recognition, and after some initial confusions, we could confirm the Neural stick could also be used on ARM based platforms such as the Raspberry Pi 3. Kochi Nakamura, who wrote the code for GPU accelerated object recognition on the Raspberry Pi 3 board, got hold of one sample in order to compare the performance between GPU and MvNCS acceleration. His first attempt was quite confusing as with GoogLeNet, Raspberry Pi 3 + MvNCS achieved an average inference time of about 560ms, against 320 ms while using VideoCore IV GPU in RPi3 board. But then it was discovered that the “stream_infer.py” demo would only use one core out of the 12 VLIW 128-bit vector SHAVE processors in Intel’s Movidius Myriad 2 VPU, and after enabling all those 12 cores instead of just one, […]

August 2, 2017August 2, 2017 by Jean-Luc Aufranc (CNXSoft) - 8 Comments

Intel’s Movidius Neural Compute Stick Supports Raspberry Pi 3 Board

Last month, Intel introduced Movidius Neural Computer Stick to accelerate applications such as object recognition, and do so offline, i.e. without the cloud, and at low power. While there was not that much information available at the time, the minimal requirements for the host machine were that it had to be a x86_64 computer running Ubuntu 16.04, and come with at least 1GB RAM, and 4GB storage. So I understood the stick would only work attached with 64-bit Intel or AMD processors, and ARM development boards would not be an option. But today, I’ve found that Movidius had uploaded a new video showing a Python based object recognition demo with the Neural Compute Stick connected to the the Raspberry Pi 3 board. You just need to add a USB camera, copy ncapi directory from the SDK installed on your Ubuntu 16.04 development machine to the Debian Jessie installed on RPi 3 […]

July 26, 2017July 26, 2017 by Jean-Luc Aufranc (CNXSoft) - No Comments

Intel Curie Module, Arduino 101 Board Are Being Discontinued (Too)

Intel seems to have completely given up on its efforts to bring products specific to the Internet of Things. After discontinuing Intel Edison, Galileo and Joule boards & modules last month, forcing companies to look for alternatives, the company has now issued product discontinuance / end-of-life notices for Intel Curie Module and Arduino 101 board, itself based on the Curie module. The two product change notification notice can be found below for: Select Intel Curie Module Products – PDF Select Intel Arduino 101 Products – PDF The use of the word “Select” would normally mean some versions of the module and board won’t be affected, but I fail to see which ones here, as AFAIK there’s only one Arduino 101 board, and two variants of Curie modules, all three to be discontinued. Arduino 101 will be phased out faster with the following milestones: July 17, 2017 – Product Discontinuance Program […]

July 21, 2017July 21, 2017 by Jean-Luc Aufranc (CNXSoft) - 8 Comments

Intel’s Movidius Neural Compute Stick Brings Low Power Deep Learning & Artificial Intelligence Offline

Intel has released several Compute Stick over the years which can be used as tiny Windows or Linux computer connected to the HDMI port of your TV or monitor, but Movidius Neural Computer Stick is a complete different beast, as it’s a deep learning inference kit and self-contained artificial intelligence (A.I.) accelerator that connects to the USB port of computers or laptops. Intel did not provide the full hardware specifications for the kit, but we do know the following specifications: Vision Processing Unit – Intel Movidius Myriad 2 VPU with 12 VLIW 128-bit vector SHAVE processors @ 600 MHz optimized for machine vision, Configurable hardware accelerators for image and vision processing; 28nm HPC process node; up to 100 gigaflops USB 3.0 type A port Power Consumption – Low power, the SoC has a 1W power profile Dimensions – 72.5mm x 27mm x 14mm You can enter a trained Caffe, feed-forward […]

July 5, 2017July 5, 2017 by Jean-Luc Aufranc (CNXSoft) - 15 Comments

Supermicro A2SDi-2C-HLN4F Server Mini-ITX Motherboard is Based on Intel Atom C3338 “Denverton” Processor

Almost exactly one year ago, I wrote about Intel Atom C3000 Series “Denverton” processor based on the same Goldmont architecture used in Apollo Lake processor, and used in an early GIGABYTE MA10-ST0 server motherboard prototype. The latter is not out yet, but according to a YouTube video, the 16-core motherboard should be launched in H2 2017. In the meantime, Supermicro also made their own Denverton motherboard based on a lower-end Atom C3338 dual core processor. Supermicro A2SDi-2C-HLN4F board specifications: Processor – Intel Atom C3338 dual core Denverton processor @ 1.50 / 2.20 GHz with 4 MB cache; 9W TDP System Memory Up to 64GB Register DIMM RDIMM and DDR4-1866MHz Up to 32GB Unbuffered ECC/non-ECC UDIMM, DDR4-1866MHz, in 2 DIMM slots Storage – 4x SATA 3 ports, but up to 8x SATA 3 (6 Gbps) ports are configurable; N.B.: total combined PCI-E lanes and SATA ports is up to 8 Connectivity […]

June 19, 2017June 20, 2017 by Jean-Luc Aufranc (CNXSoft) - 5 Comments

Intel Quark S1000 “Sue Creek” Processor to Support On-Chip Speech Recognition

Intel may have announced plans to discontinue several of their IoT boards, but based on some documents I received, the company has not given up on the Quark family, although they may have given up on the Intel architecture for low power microprocessor, as Intel Quark S1000 – codenamed “Sue Creek” – will feature two Tensilica LX6 cores (yes, just like ESP32), and is designed to handle speech recognition at the edge (e.g. locally), so some of your voice commands should still work when Internet is down. Intel Quark S1000 key features and specifications: Digital Signal Processors Dual Tensilica LX6 cores @ 400 MHz with HiFi3 DSP Single precision scalar floating-point instructions 16KB 4-way I$; 48KB 4-way D$ Up to 2400 DMIPS, 3.2 GMACS (16×16), 800 MFLOPS of Compute Speech Accelerators A GMM (Gaussian Mixture Model) and neural network accelerator Low power keyboard and limited vocabulary recognition Up to 9.6 […]