LyRa is a Raspberry Pi CM3L based Handheld Game Console (Crowdfunding)

The LyRa handheld game console is the first of its kind to carry a Raspberry Pi CM3L module inside. We reported on the Raspberry Pi Compute Module 3 Light in early 2017, and its significant features and abilities. The LyRa is being developed by Creoqode who started their campaign on Kickstarter in July 2019.  The campaign has already surpassed its funding goal, and it looks like the LyRa will become a reality. The LyRa comes in two versions 1. RTG – Ready-To-Go which is a completely assembled and ready to play handheld game console, computer and entertainment console. 2. DIY – Do-It-Yourself version comes in pieces that can be made into a fully functioning handheld game console in about 15 minutes. The unit features a Raspberry Pi CM3L module which is able to emulate literally hundreds of classic game consoles and games. The unit can attach through HDMI to a […]

MYIR Announces Xilinx Zynq UltraScale+ MPSoC SoM and Development Board

MYD-CZU3EG development board

MYIR Technology has been selling Xilinx Zynq-7000 FPGA + Arm systems-on-module since 2016, but the Chinese company has now announced new modules based on the more powerful Xilinx Zynq Ultrascale+ MPSoC with Arm Cortex-A53 cores, Arm Cortex-R5 cores, and Ultrascale FPGA fabric, as well as a corresponding development board. MYC-CZU3EG Zynq UltraScale+ MPSoC CPU Module CPU module specifications: MPSoC – Xilinx Zynq UltraScale+ XCZU3EG-1SFVC784E (ZU3EG, 784 Pin Package) MPSoC with quad-core Arm Cortex-A53 processor @ 1.2 GHz, dual-core Cortex-R5 processor @ 600 MHz, Arm Mali-400MP2 GPU, and 16nm FinFET+ FPGA fabric (154K logic cells, 7.6 Mb memory, 728 DSP slices) System Memory – 4GB DDR4 @ 2,400MHz Storage – 4GB eMMC Flash, 128MB QSPI Flash On-module chips Gigabit Ethernet PHY USB PHY Intel Power Module Clock Generator 2x Samtec 0.5mm pitch 160-pin high-speed headers bringing out Networking – Gigabit Ethernet USB – USB 2.0 interface 4x PS GTR transceivers along with […]

NXP i.MX 7ULP Enters Mass-Production, EVK and Systems-on-Module Announced

Last year, we wrote about F&S Elektronik Systeme PicoCORE MX7 system-on-module (SoM) powered by NXP i.MX 7ULP processor manufactured with a 28nm FD-SOI process, and that was announced a year earlier. The official launch of the module was planned for Q3 2018, but there have been some delays as LinuxGizmos reports NXP has only started mass production of their i.MX7 ULP this June. With the official launch of i.MX 7ULP, the company also introduced the official i.MX 7ULP Evaluation Board (MCIMX7ULP-EVK) as showcased on Element14’s community and several other companies announced i.MX 7ULP systems-on-modules. Benefits of NXP i.MX 7ULP Processor The i.MX7 ULP family of processors is an extremely power-efficient series, that is utilizing lower power for more functions. The SoC is being touted as the most power-efficient processor that can be obtained that also houses a 3D GPU.  Although like the i.MX7, the i.MX 7ULP combines both Cortex-A7 and […]

Inforce Introduces Snapdragon 660 & 845 Modules with On-Device AI

ACC-1C20 Carrier Board

Inforce Computing has just launched two new pin-compatible system-on-modules, namely Inforce 6502 and Inforce 6701, powered by Qualcomm Snapdragon 660 & Snapdragon 845 SoC respectively. In their newsletter, the company claims those are their first modules with on-device AI capabilities with the Snapdragon 660 enabling “advanced visual computing, enhanced graphics and on-device machine learning capabilities”, while the more powerful Snapdragon 845 is better suited for “immersive multimedia experiences including optimized AI performance for a more responsive, power-efficient user- experience and capture of cinema-grade videos in UHD@60fps resolution. Inforce 6502 Snapdragon 660 SoM Specifications: SoC – Qualcomm Snapdragon 660 with 8-core Kryo 260 processor, Adreno 512 GPU, Qualcomm Hexagon 680 DSP with Hexagon Vector eXtensions (HVX) for Caffe2 and Tensorflow System Memory & Storage – 3GB LPDDR4, Dual-Channel + 32GB eMMC flash in single eMCP package Connectivity Bluetooth 5.0 + Wi-Fi 5 802.11ac 2×2 with MU-MIMO and Dual Band Simultaneous (DBS) […]

Turing Pi Clusterboard Takes up to 7 Raspberry Pi Compute Modules

Turing Pi Raspberry Pi Compute-Module 3+ Cluster Board

We’ve already covered several cluster solutions based on Raspberry Pi boards such as Bitscope Blade with up to 40 Raspberry Pi boards, a 16 Raspberry Pi Zero cluster board prototype, Circumference “datacenter-in-a-box” with up to 32 Raspberry Pi 3 B+ boards. If you want something more compact, it makes sense to develop a platform with Raspberry Pi Compute Modules instead, and we’ve already published news about MiniNodes Raspberry Pi 3 CoM Carrier Board that supports up to to 5 Compute Modules 3/3+ last year. There’s now another option with Turing Pi Clusterboard support up to 7 Compute Modules for applications leveraging Kubernetes, Docker, Jupyter Notebook, machine learning (TensorFlow/Caffe), and serverless stack. Turing Pi specifications: 7x Sockets for Raspberry Pi Compute Module 3/3+ Storage – 7x microSD card slots Video Output – 1x HDMI port, MIPI DSI connector Audio – 1x 3.5mm audio jack Camera I/F – 2x MIPI CSI connectors […]

Javaino JoC Reference Board & Module Bring Java to the Embedded World

Java-Programmable module

There is a Java-programmable module on the block. The module is going to change the face of programming and make things easier. The Java-on-Chip (JoC) module was designed by the Austrian manufacturer, Demmel. This module minimizes programming and development time of smart applications. The first thing to remember is that commercially embedded systems are developed with programmable languages, and they typically employ the use of either the C language or assembly. However, Demmel has made this new module and board specifically for Java programmers. The Java-programmable module is designed to work with the Javaino JoC reference board. Another thing about the JoC module is that it radically shortens development times.  Furthermore, it simplifies PCB design efforts and also reduces programming effort. Also, the Java-programmable module comes as a tiny 24 × 36-mm module. As a matter of fact, it offers a diverse selection of interface options.  Now, these options can […]

Ivport Stereo CM Breakout board for Raspberry Pi Compute Module Supports Two Cameras

Ivport Stereo CM RPi Dual Camera

We’ve previously written about IVport V2 camera multiplexer board that can connect up to 16 cameras to a single Raspberry Pi board in order to create 360 degrees camera setups for example. The company also offered a version with two cameras for stereo recording and capturing modes. But if you’d rather use a Raspberry Pi Compute Module with or without eMMC flash, and use either Raspberry Pi camera V1 or V2, the company has launched Ivport Stereo CM breakout board with support for up to two cameras and exposing some extra ports. Ivport Stereo CM specifications: RPi module compatibility Raspberry Pi CM1 Raspberry Pi CM3 (eMMC equipped) Raspberry Pi CM3 Lite Raspberry Pi CM3+ (eMMC equipped) Raspberry Pi CM3+ Lite RPi camera compatibility Raspberry Pi Camera Module V1.3 (OV5647 sensor) Raspberry Pi Camera Module V2 (Sony IMX 219 sensor) Storage – microSD slot Video Output – HDMI Networking – 10/100M […]

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 […]

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