Uninterruptible 12VDC power supply! Powerful DC UPS module with maintenance-free Supercaps and wide-range input 16… 32VDC The robust DC2412-UPSDP2 is a maintenance-free 12VDC emergency power supply in open frame design. To protect against voltage fluctuations, flicker, voltage drops or failures in the supply voltage, the DC UPS modules are equipped with Supercaps as energy storage devices that work on the principle of double-layer capacitors (EDLC).
The SiglentSDG7000A Series of 2-channel AWGs offers models in maximum frequencies of 350MHz, 500MHz, and 1GHz, with 5Gsa/s sampling, 14-bit vertical resolution, and a memory depth of 512Mpts. Arbitrary waveforms are generated with sample rates from 0.01Sa/s to 2.5GSa/s, and vector signals at 500MSa/s. A wide variety of test signals can be generated, with the option to add 16 digital channels to the 2 analog channels to provide synchronous mixed signal analysis ability.
Saelig Company, Inc. has introduced Siglent’s SDG7000A series of dual-independent-channel arbitrary waveform generators that feature up to 1GHz bandwidth, 5Gsa/s sampling, 14-bit vertical resolution, and a memory depth of 512Mpts. Arbitrary waveforms are generated with sample rates from 0.01Sa/s to 2.5GSa/s, and vector signals at 500MSa/s. A wide variety of test signals can be generated, including CW, pulse, noise, PRBS patterns, and 16-bit digital bus patterns. The optional digital bus feature can create 16-bit LVTTL or LVDS output with a bit rate of 1ubps to 1Gbps. Combining analog channels with the digital bus can create synchronous mixed-signal outputs for complex test situations.
Pulse waveforms can have a minimum width of 1ns, and a minimum edge of 500ps pulse with low jitter, the rise/fall edge being independently and finely adjustable. The series also supports complex signals such as modulation, lin/log sweep and burst, and dual channel copying/coupling/tracking and superposition. The outputs can be differential or single-ended and the 24Vpp analog output can add a ± 12Vdc offset to provide a maximum output range of ± 24V (48V). This large output swing can eliminate the need for external power amplifiers and extend the range of suitable applications. Phase locking can align the phases of both outputs.
Three models are available in the SDG7000A series: SDG7032A (350MHz), SDG7052A (500MHz), and SDG7102A (1GHz). Optional vector signal generation includes modulation modes such as ASK, PSK, FSK, and QAM, while the accompanying EasyIQ software helps with vector signal creation and editing. All models also include a high-precision Frequency Counter. Instrument control is simplified by the 5” capacitive touchscreen, as well as external mouse and keyboard support and remote webserver operation.
The SDG7000A series supports up to 1024 arbitrary wave segments, each of which can be set with a maximum of 65535 repetitions. When switching between segments, the output seamlessly moves from the last point of the previous segment to the first point of the next segment without generating an idle level, suiting the SDG7000A series for applications with challenging requirements for waveform switching.
The Siglent SDG7000A Series of 2-channel AWGs are available now from Saelig Company, Inc.
We have already come across tens if not hundreds of Raspberry Pi’s in-house silicon tapeout RP2040 integrated boards. The RP2040 was first seen in Raspberry Pi’s own microcontroller board– RPi Pico that features a dual-core Arm Cortex-M0+ processor with 264KB internal RAM and support for up to 16MB of off-chip flash storage. To add analog functionality to the Raspberry Pi Pico, a Turkish electronics engineer, Alperen Akküncü has pre-launched PlainDAQ, an open-source DAQ module as an external hardware carrier board. For analog carrier modules, precision is one of the most important factors while designing that can change the way developers consider buying the hardware.
A data acquisition unit (DAQ) module is hardware that measures an electrical or physical parameter like the voltage, current, temperature, pressure, or sound. The PlainDAQ has a simple design with a compact form factor without wasting any space on the PCB that consists of four analog input channels multiplex on one side of the Raspberry Pi Pico-compatible female headers. These four channels multiplexed are connected to the Microchip MCP33151-05 ADC featuring a 12-bit resolution and 72dB signal-to-noise ratio while maintaining a sampling rate of 500kS/s.
After contacting the developer, he confirmed that PlainDAQ will utilize MCP33141D-05 or MCP33111D-05 (or equivalent part).
For analog outputs, the carrier board has also integrated another Microchip MCP4716 (or MCP47FEB11 or MCP4911) single-channel, 10-bit DAC with integrated EEPROM and an I2C compatible serial interface. This integrated circuit helps to create analog outputs and waveforms. Additionally, the PlainDAQ generates ±5 V voltages to offer a bipolar power supply which is very uncommon in hardware devices as they are difficult to generate. The DAQ devices include a stable voltage reference with 20 ppm/°C drift.
Before shipping the PlainDAQ, the developer promises to calibrate with DMM7510 high-resolution digital multimeter and also gives the flexibility to be calibrated “with proper equipment with the help of on-board EEPROM for storing calibration data.” In terms of wireless connectivity on the device, the schematic shows the integration of the ESP32-MINI-1 AT-Command module that not only provides a simple UART interface to Raspberry Pico hardware platform but also offers 2.4GHz IEEE802.11b/g/n Wi-Fi and Bluetooth LE 4.2 connectivity.
Implementing the DAQ device in a test use case, Alperen Akküncü gives a detailed project log on USB speed test as the PlainDAQ will be transferring a massive load of data.
“I wanted to test how fast I can transfer data via USB. I used two raspberry pi pico’s one of them doing the debugging and the other is doing the actual work.” The problem statement mentions the transfer of 6MBit/s of data from the Pico hardware platform to the computer. Interestingly, the developer could read up to more than 7Mbits/s. However, the developer also notes that “the speed depends on your setup as and if you use a hub it’s slower.”
If you are interested in the open-source PlainDAQ analog precision carrier board for Raspberry Pi Pico, consider supporting the crowdfunding campaign on CrowdSupply. For live updates, you can sign up on the product page. Also, if you want to contribute to the open-source GitHub repository, you can do so by following this link. The KiCAD project will be available for the open-source community earliest by next week. The developer has provided us with information on the pricing saying, “I can guarantee that it’s going to be in the sub-100$ region and probably very affordable.”
Alperen Akküncü will be releasing some videos to showcase what you can do with PlainDAQ. PlainDAQ has precision ADC and DAC on the same board and coupling all of these with python scripting their lots of uses. Here is a list of videos in the making:
Using PlainDAQ as a multimeter for voltage measurement and sending the measurement to a smartphone via Bluetooth
Waveform capture with PlainDAQ and use it as an oscilloscope. You can sample the signal at 500ksps and generate a waveform with PlainDAQ and send the measurement data to a computer via USB for visualization.
Measuring capacitance and inductance of components using DAC and ADC together. By generating a step signal with DAC and measuring the response with ADC, you can infer inductance or capacitance by measuring the rise time.
We thank Alperen Akküncü for providing us with all the extra information that will be coming soon. Stay tuned for more updates.
Last week, Codasip, known for edge tools and IPs, has announced RISC-V-based embedded cores for AI/ML edge customizations – L31 and L11 RISC-V processor cores. In addition to the existing low-power embedded cores, the L31 and L11 are aimed towards easing the customization process using Codasip Studio tools for deep neural network applications with low-power and limited space constraints.
The growing demand for real-time edge processing, security, and power consumption has been a top priority for researchers. The way software and hardware integration have solved these challenges are incredible while delivering sufficient performance with limited resources. The new Codasip L31 and L11 embedded cores are capable of running Google’s TensorFlowLite for microcontrollers when combined with Codasip Studio tools, enabling flexibility to customize the embedded AI cores for IoT applications.
“Licensing the CodAL description of a RISC-V core gives Codasip customers a full architecture license enabling both the ISA and microarchitecture to be customized,” says Codasip CTO, Zdeněk Přikryl. “The new L11/31 cores make it even easier to add features our customers were asking for, such as edge AI, into the smallest, lowest power embedded processor designs.”
The novel Design for Differentiation design approach by Codasip allows customers to customize their processor cores using Studio tools for a specific system, software, and application requirements. Due to the customizable option, Codasip cores have witnessed immense success with over 2 billion processor IPs present in the market.
As the new embedded cores support neural networks through the TensorFlowLiteAI framework, the L31 and L11 are considerably good choices for system developers looking for high-performance processor cores for AI/ML edge devices. Featuring all the edge computing features, Codasip’s custom-designed performance delivers real-time processor capabilities for mission-critical and embedded IoT applications.
For more details on the processor cores, visit the press release. You can also take a look at the research article titled “Embedded AI on L-series cores: Neural Networks empowered by custom instructions.”
Even after the release of a 64-bit Raspberry Pi single-board computer, the Raspberry Pi OS remained with the 32-bit version– but in the making of a 64-bit OS. After a series of testing and validation from in-house engineers, while taking feedback from the community after the beta launch, Raspberry Pi officially announces the availability of a 64-bit Raspberry Pi OS version for download to a wider audience. While developing the upgraded operating system, the main focus of the company was on the compatibility of the devices and “avoid customer confusion.”
There is a long list of Raspberry Pi models that are capable of running the 64-bit OS, including the popular Raspberry Pi 4B single-board computers and the compute module 4 while not forgetting the latest Raspberry Pi Zero 2 W tiny computers. Also, you will be requiring an external storage device, such as a MicroSD card with at least 8GB of storage space, to install the new 64-bit operating system. The Raspberry Pi Foundation realized the reasons why developers want to choose a 64-bit operating system for their application as they not only offer performance benefits but also provide flexibility to utilize many closed-source applications that are only available for arm64.
CNX-Software Team tests the 64-bit operating system [Image Source CNX-Software]In the design of Raspberry Pi 4 hardware, the team had to solve the problem concerning 32-bit pointer only allowing the user to address 4GB of memory from a single process. To solve this, the use of an ARM large physical address extension can provide access to up to 8GB of memory, but with constraining that any process is limited to accessing 3GB. Even though this was enough for most of the applications, for memory-intensive applications in Raspberry Pi OS, Chromium, several use cases will now benefit to allocate the entire 8GB of memory of Raspberry Pi from a single process.
However, if you are wanting to stream OTT applications, like Netflix and Disney+, the default version of 64-bit Chromium does not have a WidevineCDM library, in which case you will be required to choose a 32-bit version. For those who have used Mathematica on Raspberry Pi as a computational programming tool used in science, math, computing, and engineering, the Chief Product Offer at Raspberry Pi, Gordon Hollingworth notes, “currently that is available on the 32-bit OS. Will ask about supporting 64-bit.” Even though we don’t have a timeline for the support, you should be expecting it very soon.
Several known hardware developers, including Jeff Geerling, mentions
“it will cause a bit of short-term strife for some users (especially on older boards), this is a great long-term improvement so many users don’t have to spend as much time trying to get newer software working on ancient 32-bit architectures.” He goes on to add, “the efforts are often less appreciated, but go far in recommending Pis vs other SBCs.
The Raspberry Pi 64-bit operating system is available for download and a download link is available, just head to the download page for various installation options.
AI accelerators are specialized hardware designs that are built for computing complex AI workloads in the field of edge computing. While deep neural networks are assumed to be the optimized solution for image recognition and object detection, AI tasks, a group of researchers from the Tokyo Institute of Technology in Japan, proposed a hardware accelerator chip design, Hiddenite, to achieve high accuracy for the calculation of sparse hidden neural networks.
Hiddenite combines weight generation and super mask expansion to significantly reduce external memory access for improved computation efficiency. The super mask is defined by the top-k% highest scores, denotes the unselected and selected connections as 0 and 1, respectively. The hidden neural network helps reduce computational efficiency from the software side.
“Reducing external memory access is key to reducing power consumption. Currently, achieving high inference accuracy requires large models. However, this increases external memory access to load model parameters. Our main motivation behind the development of Hiddenite was to reduce this external memory access,” explains Prof. Motomura.
Hiddenite stands for Hidden Neural Network Inference Tensor Engine (Hiddenite) is the first HNN inference chip to offer benefits in reducing external memory access and increasing energy efficiency. On-chip weight generation is capable of re-generating weight using a random number generator to eliminate the requirement to access and store weights in external memory. The provision of “on-chip supermask expansion,” will considerably decrease the number of supermasks that must be loaded by the accelerator. The Hiddenite chip’s high-density four-dimensional (4D) parallel processor maximizes data re-use during the computational process and thereby improves efficiency.
“The first two factors are what set the Hiddenite chip apart from existing DNN inference accelerators,” reveals Prof. Motomura. “Moreover, we also introduced a new training method for hidden neural networks, called ‘score distillation,’ in which the conventional knowledge distillation weights are distilled into the scores because hidden neural networks never update the weights. Accuracy using score distillation is comparable to the binary model while being half the size of the binary model.”
The team used the Taiwan Semiconductor Manufacturing Company’s (TSMC) 40 nm technology to fabricate the prototype chip. With size at 3×3 mm, the chip is capable of doing 4,096 MAC (multiply-and-accumulate) operations simultaneously with the computational efficiency of up to 34.8 tera operations per second(TOPS) per watt of power while reducing the amount of model transfer to half that of binarized networks.
The research article was presented in the International Solid-State Circuits Conference 2022 under the title “Hiddenite: 4K-PE Hidden Network Inference 4D-Tensor Engine Exploiting On-Chip Model Construction Achieving 34.8-to-16.0TOPS/W for CIFAR-100 and ImageNet.”
Axiomtek – a world-renowned leader relentlessly devoted in the research, development and manufacture of series of innovative and reliable industrial computer products of high efficiency – is proud to announce the CAPA55R, a high-performance 3.5” embedded SBC powered by the 11th Gen Intel® Core™ i7/i5/i3 & Celeron® processors (code name: Tiger Lake-UP3) with 28W cTDP and Intel® Iris® Xe Graphics. The industrial-grade embedded board features triple-display capability through HDMI 1.4, LVDS and DisplayPort++ interfaces, making it well-suited for graphics-intensive applications. This industrial motherboard is designed for operational stability with a wide operating temperature range from -20°C to +60°C. The CAPA55R is optimized for various industrial IoT-related applications in the embedded market, such as industrial control, machine vision, self-service terminal, digital signage, and medical imaging.
Advanced Features:
11th gen Intel® Core™ i7/i5/i3 & Celeron® processor (Tiger Lake-UP3)
Two DDR4 SO-DIMM for up to 64GB of memory
One GbE LAN and one 2.5 GbE LAN ports
Three M.2 expansion slots
The feature-rich CAPA55R has two 260-pin SO-DIMM sockets for up to 64GB of DDR4-3200. To fully join the IoT world, this 3.5-inch SBC is equipped with one M.2 KeyE slot (PCIe x1, USB 2.0 in 22 x 30) for wireless modules, one M.2 Key B (PCIe x1, USB 3.2 Gen2, USB 2.0 in 30 x 42 or 30 x 50) slot for cellular network cards such as 4G/LTE/5G modules, and one pin-header SIM connector to expand the SIM socket with the AX93A19 module. Keeping up with the fast transmission of massive data, it also has one M.2 Key M (PCIe x4, 22 x 80) for NvME storage cards. To satisfy the increasing demands of connecting more peripheral devices, the CAPA55R also provides three USB 3.2 Gen2, four USB 2.0, one GbE LAN, one 2.5 GbE LAN, two RS-232/422/485, one SATA-600, 8-channel DIO, and one HD audio via the daughter I/O board AX93A22. In addition, the 3.5-inch embedded SBC provides a 12-24 VDC input. It supports Windows® 10 and Linux operating systems.
“The highly integrated CAPA55R is equipped with 11th gen Intel® Core™ CPUs and offers up to 64GB DDR4, 4K HDMI, 2.5 GbE LAN, three M.2 slots, and up to seven USB ports. This compact embedded board is ideally suited to embedded solutions requiring multi-tasking capabilities and full-graphics feature,” said Michelle Mi, a product manager of the Product Planning Division at Axiomtek. “Its reversed onboard CPU is attached on the rear side of the board to aid with heat dissipation and offers flexibility for easy system integration, especially in space constraint enclosure. The CAPA55R was designed to shorten the time to market and reduce development costs.”
The CAPA55R is available in April for purchase. For more product information or pricing, please visit our global website at www.axiomtek.com or contact one of our sales representatives at info@axiomtek.com.tw.
Aspinity‘s analogML core features the improved capabilities of a tinyML chip with low-power analog neuromorphic computing architecture– with a system-level approach to low-power edge processing. Without making use of power-hungry digitization and digital processors, the analogML core is a fully analog inferencing solution built on the RAMP technology platform that classifies raw, unstructured sensor data in the domain of analog systems.
The elimination of extraneous data at the very beginning of the signal chain enhances the battery life of the analogML core by 10x or more. This feature is attributed to the implementation of always-on edge applications like voice activity detection, acoustic event detection, and vibration monitoring.
In the classic always-on edge system, data relevance can only be established after digitization as the ADC and digital processors consume the majority of system power. A digitize-first architecture is inefficient and wastes significant resources in evaluating data that will be discarded. On the other hand, in an analyze-first design, the analogML core reduces this inefficiency by introducing near-zero-power inferencing into the analog domain. Data relevance is evaluated prior to digitalization, allowing higher-power digital systems to remain off unless important data is identified.
“We’ve long realized that reducing the power of each individual chip within an always-on system provides only incremental improvements to battery life,” said Tom Doyle, founder and CEO, Aspinity. “That’s not good enough for manufacturers who need revolutionary power improvements. The AML100 reduces always-on system power to under 100µA, and that unlocks the potential of thousands of new kinds of applications running on battery.”
Made up of a number of software-controlled analog processing blocks, the analogML core can be activated, altered, and customized for a variety of analyze-first applications, including smart home, IoT, consumer, industrial, and biological applications. Since it is a purely analog-based processing device, each of the processing blocks can be powered individually as needed, hence there is no clock on the analogML core. The functionalities offered by the analogML core include interfacing of sensors, analog feature extraction, analog neural network, analog data compression.
Engineers can construct, assemble, and install application-specific analog machine learning models onto the analogML core using Aspinity’s efficient development environment. The analogML core was created for engineers without analog experience in mind, this allows the engineers to use conventional training data and programming interfaces that they are currently familiar with.
For more details on Aspinity’s AnalogML core, head to the product page.
Released a few months earlier, the Challenger RP2040 Wi-Fi board comes in an Adafruit Feather form factor integrated with a Raspberry Pi RP2040 dual-core Arm Cortex-M0+ microcontroller with 8MB of flash and a Wi-Fi radio.
“This is a spin-off from our Challenger RP2040 Wi-Fi board, but we have replaced the Wi-Fi module with a low power LoRa radio module from Hope RF,” says Invector Labs in their latest board design. “The transceiver features a LoRa long range modem that provides ultra-long range spread spectrum communication and high interference immunity whilst minimizing current consumption.”
The Challenger RP2040 LoRa is almost a carbon copy of the layout and pinout, except for the LoRa transceiver module and a tiny U.FL connector for an external antenna. Physical boot and reset buttons, a connector and charge circuit for an optional battery, a USB Type-C connector for power and transmission, and sparsely populated connectors across both sides that match the Feather pinout are all still present.
Specifications of Challenger RP2040 LoRa
MCU – Raspberry Pi RP2040 dual-core Cortex-M0+ MCU at 133MHz with 264KB SRAM
Wireless module – RF RFM95W connected to RP2040 via SPI channel and some GPIOs
Power Supply: 5V via USB Type-C port
Features a 2-pin LiPo battery connector in addition to the LiPo charger circuit with 250mA charging current
The integrated LoRa module in the RFM95W transceiver offers the provision of a long-range spread spectrum communication and high interference immunity while consuming minimal current. Outperforming the classic modulation approach in terms of blocking and security, LoRa resolves the traditional design compromise involving range, interference immunity, and energy usage.
When it comes to software compatibility, the Challenger RP2040 LoRa follows the same software support as the RPi Pico due to the identical microcontroller integrated both the hardware– compliant with Arduino IDE, Circuitpython, Micropython.
Listed on the Invector Lab store, the product is currently out of stock, however, more information on the product can be found on the official website of ILabs.
Penk Chen designed a pocket-sized computer with a retro appeal, built from either off-the-shelf parts or 3D-printed structures. Inspired by the retro-style, powered by the Raspberry Pi Zero 2 W, Penkesu is a portable computer with a 7.9-inch widescreen display and a 48-key ortholinear mechanical keyboard.
Penkesu isn’t Chen’s first attempt at building a Raspberry Pi-powered portable tablet while Chen had already designed the CutiePi tablet. This commercial tablet-style device solved the problem of the cumbersome and messy desks covered in development boards and peripherals. Staying confined to a charging station or a desk was now a thing of the past, with its 8-inch touchscreen, CutiePi came with a 5000 mAh battery allowing you to work on the go.
The enclosure for the Penkesu computer is built around the monitor and keyboard, thereby resulting in relatively smaller physical dimensions. Like most portable computers, Penkesu is designed to fold in half when not in use, safeguarding the ultra-wide display. Furthermore, with the intent to keep the hinge design minimal, the repurposed Gameboy Advance SP hinges and HDMI ribbon cable are utilized, they efficiently bear the weight of the display so it doesn’t tip over.
The Arduino Pro Micro powers the board, which enables it to run the QMK open-source keyboard firmware. The compact design of the keyboard was originally developed by the [larrbo] that had been open-sourced– later tweaked the layout so it fits the requirements of Penkesu.
All the 3D printable files, bills of the materials, and the instructions for assembly are all accessible on the project’s website. More information on the same can be found on Chen’s GitHub repository.
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