Open Source Hardware SpiderSoM System-on-Module Features Intel MAX 10 FPGA

aries embedded spidersom

When embedded systems companies offer a system-on-module (SoM) and a baseboard, the later is usually open source hardware with all design files provided so that customers can leverage the work for their own baseboard, but files for the SoM are normally not released to customers. Intel MAX 10 FPGA based Aries SpiderSoM and SpiderBase change that, as the company has decided to release both the module and carrier board KiCAD designs under a CERN OHL v1.2 license. SpiderSoM system-on-module specifications: FPGA – Intel MAX 10 FPGA in F256 package from 10M04DC to 10M50DA System Memory – Optional 128/256/512MB DDR3 DRAM for 10M 16/25/40/50 FPGAs Storage – Optional 4 MB SPI NOR, optional 4 GB eMMC flash 230-pin MxM2 edge connector with 178x FPGA GPIO pins, including 13 LVDS transmitters and 54 receivers Misc – RTC with battery backup,  programmable clock generator and PLL, with optional external reference input Power Supply Programmable high-efficient PMIC, FPGA IO voltages are configurable optional Li-Ion/Li-Pol …

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Intel Neural Compute Stick 2 with Myriad X VPU Finally Announced

Intel Neural Compute Stick 2

Intel Neural Compute Stick was first introduced in early 2017 as a USB compute that allows AI inference at the edge with low power consumption. The stick is based on Myriad Movidius 2 VPU (Vision Processing Unit), and was found to significantly improve inference performance on Raspberry Pi 3 board over a proprietary GPU accelerated solution. However, a little later last year, Intel also announced Movidius Myriad X VPU with claims of up to 10 times DNN performance over Myriad 2 VPU. But so far, we would only see solutions launched with the latter, and it looks like Intel is finally ready to bring Myriad X VPU to the market with the company announcing Intel Neural Compute Stick 2 at Intel’s artificial intelligence (AI) developer conference in Beijing taking place on November 14 and 15. Intel NCS 2 (Neural Compute Stick 2) specifications: Processor – Intel Movidius Myriad X Vision Processing Unit (VPU) with 16 SHAVE cores (128-bit VLIW Vector Processors) …

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BladeRF 2.0 USB 3.0 Software Defined Radio Launched for $480 and Up

BladeRF 2.0 Micro

Around 5 years ago, several affordable FPGA based open source software defined radio boards launched including HackRF, BladeRF x40 / x115, and  USRP B200. The company behind BladeRF has now launched an update of their boards with Blade RF 2.0 coming in two versions namely bladeRF 2.0 micro xA4 and bladeRF 2.0 micro xA9 supporting the same 47MHz to 6GHz frequency range, and 61.44MHz sampling rate, but the latter comes with a more powerful 301KLE Cyclone V FPGA. BladeRF 2.0 hardware specifications: FPGA Micro xA4 – Intel / Altera Cyclone V FPGA with 49 kLE Micro xA9 – Intel / Altera Cyclone V FPGA with 301 kLE Analog Devices RF Transceiver 47 MHz to 6 GHz frequency range 2×2 MIMO, 61.44 MHz sampling rate 56 MHz filtered bandwidth (IBW) Automatic gain control (AGC) Real- time custom gain control tables controlled via SPI and discrete external input pins Automatic IQ and DC offset correction 128-tap digital FIR filtering USB 3.0 SuperSpeed …

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Raspberry Pi 3 and Movidius Neural Compute Stick to the Rescue Against Child Pornography

Raspberry Pi Powered Nudity Filter

Warning note: While there won’t be any NSFW photos in this post, there will be some photos of ladies in light clothing (e.g. bikini) and “naked” animals for testing purpose… Intel released Movidius Neural Compute Stick allowing low power image recognition at the edge earlier this year, and we’ve seen it work just fine with Raspberry Pi 3 board delivering three times the performance against an inference solution leveraging VideoCore IV GPU. Christian Haschek owns a photo hosting site (PictShare) which happens to run open source code with the same name, and allows user to upload images anonymously. However, he soon found out that at least one user uploaded some child pornography. He contacted the authorities, but then wondered whether there may be others? Since there are simply too many photo on the website to look it up manually, he decided to look for a solution, and went with a few Raspberry Pi 3 boards, some Neural Compute Sticks, as …

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Arduino Unveils its First FPGA Board with MKR Vidor 4000, Updates UNO WiFi Board with ATmega4809 MCU

Arduino has been busy with new boards announcements in the last week, as after introducing MKR WiFi 1010 and MKR NB 1500 IoT boards with respectively WiFi + Bluetooth, and NB-IoT + eMTC connectivity, the company has now unveiled two new boards. MKR Vidor 4000 is the very first official Arduino board with an FPGA, while UNO WiFi Rev2 is an upgrade of UNO WiFi board featuring the new Microchip ATmega4809 MCU and an U-blox wireless module instead of an ESP8266 chip. MKR Vidor 4000 Preliminary specifications: FPGA part FPGA – Intel Cyclone FPGA with 16K Logic Elements, 504Kbit of embedded RAM and 56 18×18 bit HW multipliers for high-speed DSP System Memory – 8 MB SDRAM Storage – 2 MB QSPI Flash (1MB for user applications) Micro HDMI connector MIPI camera connector mini PCIe connector with up to 25 user programmable pins MCU – Microchip SAMD21 Cortex-M0+ 32bit low power Arm MCU  @ 48 MHz with 256 KB flash, …

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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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Quokka IoT FPGA Board is Programmable with C# Language (Crowdfunding)

Quokka FPGA IoT Controller is a board based on Altera Cyclone IV FPGA with a WiPy module for connectivity, and various I/Os that allows you to make robotics projects for example. While you could program the FPGA using VHDL, the developer – Evgeny Muryshkin – also designed Quokka Development Toolkit (QDT), a cloud-based SaaS, allowing to program FPGA with a high-level programming language, currently C#, so that software people can more easily become involved in FPGA development. Quokka IoT (preliminary) hardware specifications: FPGA – Intel Altera Cyclone IV, 6K logic elements, EP4CE6E22C8 Clock – 50MHz Connectivity – WiFi via WiPy module Expansion 40x GPIO (3 banks by 8 pins, with direction and voltage (3.3V or 5V) configuration, 16 raw IO pins 3.3V) 2x Dual Channel 10 bit ADC (3.3V) 2x Dual Channel 10 bit DAC (3.3V) H-Bridge for DC motors with support for external power Power Supply – 5-24V DC input The specifications are preliminary, because the FPGA may be replaced …

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Intel Stratix 10 MX FPGA Integrates High Bandwidth Memory DRAM (HBM2)

Intel started sampling (Altera) Stratix 10 ARM + FPGA SoC in late 2016, and now the company has announced the availability the new Stratix 10 MX FPGA family wih High Bandwidth Memory DRAM (HBM2). The latter allow Stratix 10 MX FPGAs to offer up to 10 times the memory bandwidth when compared with standalone DDR memory solutions. The higher bandwidth will be useful for multi-function accelerators for high-performance computing (HPC), data centers, network functions virtualization (NFV), and broadcast applications. Intel / Altera Stratix 10 MX SoC key features and specifications: Processor – Quad-core ARM Cortex-A53 MP Core up to 1.5 GHz Logic Density Range – 1.092M LE to 2.073M LE Embedded Memory 3.5 to 8GB HBM2 high-bandwidth DRAM memory 45 Mbit to 90 Mbit  eSRAM memory 86 Mbit to 134 Mbit M20K memory 6 Mbit to 11 Mbit MLAB memory Up to 7,920 18 x 19 Multipliers Up to 72 Transceivers up to 30 Gbps data rate (Chip to Chip) …

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