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Dialog DA16200 WiFi One Year Battery Life

May 27, 2020May 27, 2020

Dialog DA16200 WiFi SoC Promises a Year of Battery Life for Always-Connected IoT Devices

WiFi is omnipresent, and many IoT devices are relying on the wireless standard for connectivity, but as most of you will already know, WiFi suffers from high-power consumption and may not always be suitable for battery-operated devices. There are always battery optimization tricks to use WiFi on battery-powered devices, for example, a solar camera using PIR to only record and transmit video when needed, or a board optimized to consume as little as possible in deep sleep waking up only when necessary. Those tricks are not applicable to all use cases, so Dialog Semiconductor has launched DA16200 Wi-Fi SoC delivering year plus battery life for always-connected Wi-Fi IoT devices. DA16200 key features and specifications: MCU Core – Arm Cortex M4F @ up to 160 MHz Memory – 512 KB SRAM Storage – 256 kB ROM, 8KB OTP ROM, external flash controller, eMMC, SD host Connectivity 2.4 GHz, 20 MHz channel […]

May 21, 2020May 21, 2020

MARK AI Robot Kit Aims to Teach AI & Robotics to 12+ Years Old (Crowdfunding)

We’ve written about Kendryte K210 RISC-V AI processor, and Sipeed M1 module several times including in our getting started for Maixduino and GroveAI HAT boards for low-power AI inference such as object recognition or face detection using Arduino and Micropython programming. Shenzhen-based Tinkergen, a STEM Education owned by Seeed Studio, has now leveraged the low-cost processor to design MARK AI robot kit, where MARK stands for Make A Robot Kit, in order to processor an educational AI Robotics platform for children ages 12 years old and more. MARK will ship as a kit with the main parts and components including a chassis, a cover, two wheels, stepper motors, a pan-tilt camera with K210 processor, a 2.4″ LCD display, Grove & Arduino compatible MARKduino interface board, some sensors, and six AA batteries. Tinkergen offers pre-trained model to recognized objects like humans, books, pens, or smartphones, as well as traffic signs, numbers […]

May 19, 2020October 28, 2020

Olimex ESP32-S2-Devkit-LiPo WiFi Board Consumes as Little as 2uA in Sleep Mode

When we covered ESP32 powered TTGO T-Watch-2000 smartwatch this week-end, people noted that with a 350 mAh battery, the watch would last about 3.65 days considering a 4mA drain with the screen always off, WiFI and Bluetooth off, and around 65mA when the screen is on good for about 5 hours of continuous use without Bluetooth nor WiFi. But it’s possible to make a much more-efficient ESP32-S2 Wifi board, as demonstrated by Olimex with their upcoming ESP32-32-Devkit-LiPo based on ESP32-S2-Saola-1 with circuitry to support LiPo batteries. They designed the board with an ultra-low-power power supply circuit which makes current consumption during sleep only 6uA, 4 of which are due to the battery measurement resistor divider, meaning the board should consume only 2uA in sleep mode or about 10 times less than other ESP32 Olimex boards. When reviewed Qoitech Otii power measurement & DAQ tool, we also noted power consumption could […]

April 29, 2020

BashTop is a Linux Resource Monitor for the Terminal

Neil Amstrong of BayLibre recently added ODROID-C4 support to Armbian, fired up Rosetta@Home on the Amlogic S905X3 SBC, and took a screenshot of some kind of advanced htop program showing the Rosetta@Home and other processes running. And… Rosetta@Home starting ! pic.twitter.com/w10hjwppLR — Neil Armstrong @[email protected] (@Superna9999) April 27, 2020 The program used happens to be BashTop a recently released Linux resource monitor written in Bash and running in a terminal. Installing the script and running it is super easy:

git clone https://github.com/aristocratos/bashtop
cd bashtop
./bashtop

git clone https://github.com/aristocratos/bashtop

cd bashtop

./bashtop

I tried it in an AMD Ryzen 7 laptop running Ubuntu 18.04. You’ll need at least a 80×25 terminal window, but it looks much better in full screen. It shows CPU use in graphical and text forms, memory and storage usage, a list of processes, as well as network usage both in graphical and text forms. You can also select each individual process to get more information or kill it. […]

MaixCube All-in-one K210 Development Platform

April 21, 2020

Sipeed MaixCube is a Fully Integrated AI Development Platform Powered by Kendryte K210 RISC-V SoC

Sipeed has made several boards and kits based on Kendryte K210 RISC-V processor for low-power AI workloads such as face detection or object recognition including Maixduino board and Grove AI HAT that ship with camera and display. The company has now come up with MaixCube all-in-one development platform that houses Sipeed M1 module, a display, a camera, and a battery into a plastic case that’s somewhat similar to MStack M5StickV but with a larger display, and variations in the form factor and features. Sipeed MaixCube specifications: SoC – Kendryte K210 dual-core 64-bit RISC-V processor @ 400 MHz (overclockable to 600 MHz) with FPU, 8MB SRAM, KPU AI accelerator, APU audio processor, and FFT accelerator Storage – 128 Mbit flash, MicroSD card slot Display – 1.3″ TFT screen with 240×240 resolution Camera – OV7740 sensor (VGA camera) Audio – Built-in microphone, external speakers support; ES8374 audio codec USB – 1x USB […]

April 15, 2020July 31, 2020

ADLink Launches Vizi-AI Development Starter Kit for Industrial Machine Vision & Artificial Intelligence

ADLINK has recently launched Vizi-AI development starter kit for industrial machine vision and artificial intelligence (AI) at the edge in collaboration with Intel and Arrow Electronics. Vizi-AI is comprised of a carrier board that looks to be the same as used in the company’s I-Pi SMARC development kit equipped with an Intel Movidius Myriad X VPU and combined with LEC-AL Intel Atom Apollo Lake SMARC computer module. Vizi-AI SBC Let’s have a look at the hardware features and specifications of Vizi-AI SBC (aka VIZI-AI LEC-AL-E3940-AI-4G-32G): SoC – Intel Atom x5-E3940 quad-core Apollo Lake-I processor @ up to 1.6 / 1.8 GHz (Turbo) with 12EU Intel HD Graphics 500; 9.5W TDP System Memory – 4GB LPDDR4 (Option up to 8GB) Storage – 1x MicroSD card slot AI Accelerator – Intel Movidius Myriad-X VPU (Vision Processing Unit) Video – 1x HDMI port, single-channel LVDS/eDP interface via flat cable Audio – On-carrier audio […]

April 13, 2020

Grove Beginner Kit for Arduino Features Arduino UNO Compatible Board & Ten Pre-wired Modules

Arduino boards are great to get started with electronics has they offer an ecosystem of expansion modules and libraries, as well as tutorials, that may it easy to get started with almost any projects. Seeed Studio Grove is a family of standardized modules with 4-pin headers using digital I/O, analog I/O, UART or I2C interfaces and allowing you to easily connect to compatible board such as Seeeduino Lotus board. You still need to connect the Grove module via cables, so Seeed Studio decided to create a big board called Grove Beginner Kit for Arduino that features Seeduino Lotus at the center and ten pre-wired and detachable Grove modules so no cabling is required to get started apart from a USB cable. List of Grove Beginner Kit for Arduino board and modules: Seeeduino Lotus ATmega320p board compatible with Arduino UNO compatible board and featuring 12 Grove connectors 10 pre-wired modules without […]

April 1, 2020October 9, 2020

Edgeless EAI-Series Dual Arm Cortex-M4 MCU Features a 300 GOPS CNN-NPU

Microcontrollers will have an important role to play in AIoT (AI + IoT) applications as they provide the lowest cost and power consumption. Performance is limited but we start seeing MCUs with AI accelerators such as GreenWaves GAP9 multi-core RISC-V microcontroller or Kendryte K210 RISC-V MCU with a KPU AI accelerator. Another option is by Edgeless Semiconductor Co. Ltd (零边界集成电路有限公司) based in Zhuhai, China, and more specifically its Edgeless EAI-Series dual-core Arm Cortex-M4 microcontrollers equipped with a 300 GOPS CNN NPU. Edgeless EAI specifications: CPU – Dual Arm Cortex-M4F @ up to 200Mhz, with DSP instructions, I/D cache for high performance; 500DMIPS/1.25DMIPS/MHz (Dhrystone2.1) AI Accelerator – CNN-NPU clocked at up to 300MHz with 300 GOPS peak throughput; 144MAC/cycle, EER up to 1TOPS/W, for image recognition scenario. Support major CNN Models including Resnet-18, Resnet-34, Vgg16, GoogleNet, Lenet, etc.. Support Convolutional kernel size 1~7 Support Channel/Feature No. up to 512 Support Max/Average […]