BIWIN TF200 Series MicroSD: Enhanced <span style='color:red'>Raspberry Pi</span> 4B Compatibility
Trade news

BIWIN TF200 Series MicroSD: Enhanced Raspberry Pi 4B Compatibility

　　BIWIN's TF200 series microSD cards have passed the Raspberry Pi 4B AVL certification, ensuring compatibility and adaptability with Raspberry Pi microcomputers.　　The TF200 microSD cards underwent rigorous tests on the Raspberry Pi 4 Model B/4GB platform. These tests included loading each test card with a custom image (including the latest Bullseye image), an automated stress test script, and a locally accessible website using Google Puppeteer for automated testing. After over 15,000 power-off tests and over 2.7 million seconds of continuous operation, the product demonstrated a continuous write speed of over 26 MB/s, a random write speed of up to 717 IOPS, and a random read speed of up to 3525 IOPS. These results meet the testing benchmarks required by Raspberry Pi, ensuring efficient and stable operation of the Raspberry Pi devices.　　The TF200 series microSD cards feature firmware functionalities such as garbage collection and bad block management to ensure stable data recording without frame loss. The product reaches the U3 speed class and V30 video speed class, with sequential read and write speeds of up to 158 MB/s and 113 MB/s respectively, supporting 4K RAW ultra-high-definition video capture and high-speed continuous shooting. Leveraging the company's advanced packaging technologies like multi-layer stacking and ultra-thin Die, the product offers capacities up to 256 GB (with future releases of 512 GB and 1 TB). It also supports flash wear leveling technology with a P/E Cycle of 3000 times. With operational temperature ranges from -25 °C to 85 °C and features such as waterproofing, shock resistance, and temperature shock resistance, the product is well-equipped to handle various complex environmental challenges.　　The TF200 series microSD cards combine high stability, reliability, and durability, making them compatible with mainstream terminal devices. They are suitable for fields like video surveillance, digital education, industrial tablets, and dashcams. Leveraging its expertise in storage solution development and advanced testing, BIWIN can tailor storage device performance, reliability, and power consumption to meet the testing and certification requirements of SoC chips and system platforms. This adaptability ensures a high degree of compatibility with different platforms, effectively meeting the diverse storage needs of various terminal applications.
Key word：
Release time：2024-01-26 13:44 reading：2132 Continue reading>>
UK maker community turns to <span style='color:red'>Raspberry Pi</span>
Trade news

UK maker community turns to Raspberry Pi

The Raspberry Pi is becoming more popular with UK makers as nearly half of Brits (48 per cent) say they plan to use it for an electronics project, with 87 per cent of makers having already used the mini-PC to make a smart home device.Research conducted by online retailer reichelt found that there was a clear upward trend towards home-made smart technology as the use of these devices become increasingly popular for day to day life.The expanding capabilities of mini-PCs like Raspberry Pi are a key factor behind this rise in popularity, with 24 per cent of those questioned saying that they were planning to make their own smart home device, such as smart lighting, thermostats or alarms, using a Raspberry Pi board.The most popular smart gadgets were found to be security alarms – 26 per cent have made their own versions, followed by smart lighting or heating (24 per cent).Nearly half (48 per cent) of those surveyed said they plan to use the Raspberry Pi for an electronics project in the future.According to reichelt‘s research, the Raspberry Pi is proving so popular because of its:Easy installation – 20 per centLow price point – 13 per centSmall format – 11 per centInternet capability – 11 per centWhen it came to future updates and new releases, more than half of those surveyed want even more memory (54%) and even more computing power (53%) for the next Raspberry Pi model.Top Raspberry Pi projectsRaspberry Pi’s are most commonly used for:Computer for every day use – 31 per centPrinter – 29 per centMultimedia centre – 22 per centGaming – 22 per centMulti-room audio – 18 per centRemote control for devices – 18 per centWhilst there is an upward trend for more maker projects, the UK is significantly behind Germany with Germans using Raspberry Pi nearly twice as much as – 47 per cent vs one fifth.According to Sven Pannewitz, product manager for Active/Passive Components at reichelt, the affordability and versatility of the Raspberry Pi is the key driver behind this trend."The Maker projects are definitely showing a positive market development and we are seeing more developer boards being used. The projects are getting smarter, especially when it comes to home automation, because the Raspberry Pi can be used so effectively in a variety of ways.“The WiFi connection, which has been integrated since version 3, and the new PoE capability in the new 3B+ model, make the Raspberry Pi particularly interesting for smart home projects. The trend for future projects points primarily to lighting and heating control in the smart home, but the Raspberry Pi is also frequently being used as a multimedia centre."
Key word：
Release time：2018-04-25 00:00 reading：3248 Continue reading>>
New <span style='color:red'>Raspberry Pi</span> Improves Processing, Connectivity
Trade news

New Raspberry Pi Improves Processing, Connectivity

　　The Raspberry Pi Foundation launched the latest version of its module featuring significant improvements in performance, wireless connectivity and wireless circuitry certification to enable users to reduce the cost of conformance testing.　　The new Raspberry Pi 3 Model B+, which maintains the $35 price tag of the previous model, features a 1.4GHz 64-bit quad-core ARM Cortex-A53 CPU, dual-band 802.11ac wireless LAN and Bluetooth 4.2, faster Ethernet (Gigabit Ethernet over USB 2.0), power-over-Ethernet support (with separate PoE HAT) and improved thermal management. Alongside a 200MHz increase in peak CPU clock frequency, the company says the new module has roughly three times the wired and wireless network throughput, and the ability to sustain high performance for much longer periods.　　One of the key features of the module is the radio certification. The wireless circuitry is encapsulated under a metal shield, which has allowed the company to certify the entire board as a radio module under FCC rules, which in turn will significantly reduce the cost of conformance testing Raspberry Pi-based products.　　This is significant, especially for the company’s commercial base, according to Eben Upton, co-founder of the Raspberry Pi Foundation, speaking to EE Times. The company may have started out with a mission to bring more people into electronics design and computer programming, but the module is also a commercial success in mainstream products.　　“We've seen a lot of people designing Raspberry Pi into their own products, and this has become an important part of our commercial business, which of course funds our charitable work," Upton said. "Many of the features we've incorporated into 3+, particularly the modular certification and thermal improvements, have been driven by feedback from the design community.”　　The implication of this is that if customers build the new module into a product, compliance of the Wi-Fi and radio part is already covered, so the cost and time required for achieving conformance is reduced.　　Upton also said that next generation of products will be driven more by what designers are looking for. “While we have a policy of not discussing future products, with Raspberry Pi 3B+ out of the way we'll be turning our attention to what we do next," he said. "We'll be speaking to the design community about what they'd like to see in a next-gen Raspberry Pi, but I'd be surprised to see any significant change in the feature mix: it's likely to be "more" of everything we have today.”　　Asked about the global market for the Raspberry Pi, Upton responded, “Our largest market is North America, followed by the UK and Germany. We're seeing some larger companies designing it in now, alongside smaller entrepreneurial companies and individuals, which remain an important part of our strategy.”　　Upton cited examples from last year such as NEC, which introduced intelligent large format displays with Raspberry Pi connectivity, and NComputing, which introduced a cloud-ready, dual-screen capable and Wi-Fi ready thin client for Windows and Linux, built on the Raspberry Pi 3.　　The new product is built around BCM2837B0, an updated version of the 64-bit Broadcom application processor used in Raspberry Pi 3B, which incorporates power integrity optimizations, and a heat spreader. Together these enable higher clock frequencies (or to run at lower voltages to reduce power consumption), and to more accurately monitor and control the temperature of the chip.　　Dual-band wireless LAN and Bluetooth are provided by the Cypress CYW43455 combo chip, connected to a Proant PCB antenna similar to the one used on Raspberry Pi Zero W. Compared to its predecessor, Raspberry Pi 3B+ delivers somewhat better performance in the 2.4GHz band, and far better performance in the 5GHz band.　　Previous Raspberry Pi devices have used the LAN951x family of chips, which combine a USB hub and 10/100 Ethernet controller. For Raspberry Pi 3B+, Microchip provided an upgraded version, LAN7515, which supports Gigabit Ethernet. While the USB 2.0 connection to the application processor limits the available bandwidth, the module still sees roughly a threefold increase in throughput compared to Raspberry Pi 3B.　　The module uses a magjack that supports Power over Ethernet (PoE), and bring the relevant signals to a new 4-pin header. A PoE HAT will also be launched soon which can generate the 5V necessary to power the Raspberry Pi from the 48V PoE supply.　　The improved power integrity of the BCM2837B0 package, and the improved regulation accuracy of its new MaxLinear MxL7704 power management IC, has allowed the company to tune clocking and voltage rules for both better peak performance and longer-duration sustained performance.　　Below 70°C, these improvements increase the core frequency to 1.4GHz. Above 70°C, it drops to 1.2GHz, and the improvements are used to decrease the core voltage, increasing the period of time before it reaches a 80°C thermal throttle; the reduction in power consumption is such that many use cases will never reach the throttle. The company says that, like a modern smartphone, it treats the thermal mass of the device as a resource, to be spent carefully with the goal of optimizing user experience.　　The company also highlights that the Raspberry Pi 3B+ does consume substantially more power than its predecessor, so it strongly encourages designers to use a high-quality 2.5A power supply.
Key word：
Release time：2018-03-20 00:00 reading：3102 Continue reading>>
IBM Uses Deep Learning to Train <span style='color:red'>Raspberry Pi</span>
Trade news

IBM Uses Deep Learning to Train Raspberry Pi

　　Computations requiring high performance computing (HPC) power may soon be done in the palm of your hand thanks to work done this summer by IBM Research in Dublin, Ireland.　　While scientists have come a long away in teaching machines how to process images for facial recognition and understand language to translate texts, IBM researchers focused on a different problem: how to use artificial intelligence (AI) techniques to forecast a physical process. In this case, the focus was on ocean waves, using traditional physics-based models driven by external forces, such as the rise and fall of tides, winds blowing in different directions, the depth and physical properties of water influence the speed and height of the waves.　　HPC is normally essential to resolve the differential equations that encapsulate these physical processes and their relationships, and the expense often limits the spatial resolution, physical processes and time-scales that can be investigated by a real-time forecasting platform. In an interview with EE Times, IBM Research Senior Research Manager Sean McKenna said an HPC cluster using Big Iron has generally been the solution to dealing with the heavy computational load. IBM Research wanted to see if it could do the same work more quickly and more simply, he said.　　The differential equations approach has developed over the course of a century or more, he said. Machine learning through AI is not rule based. “It's non-linear mapping of one input space to an output space," McKenna said. "That's what everything is in AI right now."　　Researchers developed a deep-learning framework that provides a 12,000 percent acceleration over these physics-based models at comparable levels of accuracy. McKenna said the validated deep-learning framework can be used to perform real-time forecasts of wave conditions using available forecasted boundary wave conditions, ocean currents, and winds.　　“The deep learning method is more of a black box," he said. "It's a little bit of paradigm shift."　　Deep learning isn't about physical modeling and science to figure out what's leading to a set of results, it's about using engineering to solve a problem, and being able to do it more efficiently and faster, said McKenna. “We can build a model, train that model and put in on a more computationally-efficient device," he said.　　What is clear are the significant benefits. Massively reducing the computational expense means simulations can be done on a Raspberry Pi rather HPC infrastructure.　　The deep-learning framework was trained to forecast wave conditions at a case-study site at Monterey Bay, Calif., using the physics-based Simulating WAves Nearshore (SWAN) model to generate training data for the deep learning network. Driven by measured wave conditions, ocean currents from an operational forecasting system, and wind data, the model was run between the beginning of April 2013 and end of July 2017, generating forecasts at three-hour intervals to provide a total of 12,400 distinct model outputs. The study expands and builds on a collaboration between IBM Research-Ireland, Baylor University and the University of Notre Dame.　　The deep learning model has yet to be deployed to a physical device, said McKenna, but the study demonstrates that the reduction in computational expense means the simulation of a physics model could be done an Raspberry Pi or any other low-end computing device that's trained by HPC.　　“That opens up possibilities as to where that model can be deployed," McKenna said.　　Being able to accurately forecast ocean wave heights and directions are a valuable resource for many marine-based industries as they often operate in harsh environments where power and computing facilities are limited. One scenario includes a shipping company using highly accurate forecasts to determine the best voyage route in rough seas to minimize fuel consumption or travel time. A surfer could get data localized to a specific beach to ride the best waves, said McKenna.　　IBM Research's deep learning model could potentially be leveraged to use existing HPC infrastructure to train cheaper computing devices, even a smartphone, he said. “HPC resources are becoming more available in the cloud, so even if you don't own that resource you probably have access to it," he said.
Key word：
Release time：2017-09-29 00:00 reading：1222 Continue reading>>

Turn to

/ 1

Popular categories
Semiconductors Circuit Protection Electromechanical Embedded Solutions Connectors, Interconnects Sensors, Transducers Optoelectronics Development Kits Test and Measurement Power Fans, Thermal Management Boxes, Enclosures, Racks Cables, Wires Passive Components Others
  • Week of hot material
  • Material in short supply seckilling
model brand Quote
CDZVT2R20B ROHM Semiconductor
RB751G-40T2R ROHM Semiconductor
TL431ACLPR Texas Instruments
MC33074DR2G onsemi
BD71847AMWV-E2 ROHM Semiconductor
model brand To snap up
ESR03EZPJ151 ROHM Semiconductor
BP3621 ROHM Semiconductor
IPZ40N04S5L4R8ATMA1 Infineon Technologies
STM32F429IGT6 STMicroelectronics
TPS63050YFFR Texas Instruments
BU33JA2MNVX-CTL ROHM Semiconductor
Hot labels
ROHM
IC
Averlogic
Intel
Samsung
IoT
AI
Sensor
Chip
Original authorized brand
More Licensed Brands>>
Information leaderboard
  • Week of ranking
  • Month ranking

About us

Qr code of ameya360 official account

Identify TWO-DIMENSIONAL code, you can pay attention to

AMEYA360 mall (www.ameya360.com) was launched in 2011. Now there are more than 3,500 high-quality suppliers, including 6 million product model data, and more than 1 million component stocks for purchase. Products cover MCU+ memory + power chip +IGBT+MOS tube + op amp + RF Bluetooth + sensor + resistor capacitance inductor + connector and other fields. main business of platform covers spot sales of electronic components, BOM distribution and product supporting materials, providing one-stop purchasing and sales services for our customers.

Please enter the verification code in the image below：

verification code