Tech News - Single Board Computers

Google teased its new hardware products built around its Edge TPU at the Google Next conference last summer. Yesterday, it officially launched the Coral dev board, a Raspberry-Pi look-alike, which is designed to run machine learning algorithms ‘at the edge’, and a USB accelerator. Coral Development Board The “Coral Dev Board” has a 40-pin header that runs Linux on an i.MX8M with an Edge TPU chip for accelerating TensorFlow Lite. The board also features 8GB eMMC storage, 1GB LPDDR4 RAM, Wi-Fi and Bluetooth 4.1. It has USB 2.0/3.0 ports, 3.5mm audio jack, DSI display interface, MIPI-CSI camera interface, HDMI 2.0a connector, and two Digital PDM microphones. Source: Google Coral dev board can be used as a single-board computer when you need accelerated ML processing in a small form factor.  It can also be used as an evaluation kit for the SOM and for prototyping IoT devices and other embedded systems. This board is available for $149.00. Google has also announced a $25 MIPI-CSI 5-megapixel camera for the dev board. USB Accelerator The USB Accelerator is basically a plug-in USB 3.0 stick to add machine learning capabilities to the existing Linux machines. This 65 x 30 mm accelerator can connect to Linux-based systems via a USB Type-C port. It can also work with a Raspberry Pi board at USB 2.0 speeds. The accelerator is built around a 32-bit, 32MHz Cortex-M0+ chip with 16KB of flash and 2KB of RAM. Source: Google The USB Accelerator is available for $75. Developers can build Machine Learning models for both the devices in TensorFlow Lite. More information is available on Google’s Coral Beta website. Coming soon are the PCI-E Accelerator, for integrating the Edge TPU into legacy systems using a PCI-E interface. Also coming is a fully integrated System-on-Module with CPU, GPU, Edge TPU, Wifi, Bluetooth, and Secure Element in a 40mm x 40mm pluggable module. Google expands its machine learning hardware portfolio with Cloud TPU Pods (alpha). Intel acquires eASIC, a custom chip (FPGA) maker for IoT, cloud and 5G environments Raspberry Pi launches it last board for the foreseeable future: the Raspberry Pi 3 Model A+ available now at $25.

Yesterday, Raspberry launched the Raspberry Pi 3 Model A+ board which is a smaller and cheaper version of the Raspberry Pi 3B+. In 2014, the first gen Raspberry Pi 1 Model B+ was followed by a lighter Model A+ with half the RAM and removed ports. This was able to fit into their Hardware Attached on Top (HAT). Until now there were no such small form factor boards for the Raspberry Pi 2 and 3. Size is cut down but not the features (most of) The Raspberry Pi 3 Model A+ retains most of the features and enhancements as the bigger board of this series. This includes a 1.4GHz 64-bit quad-core ARM Cortex-A53 CPU, 512MB LPDDR2 SDRAM, and dual-band 802.11ac wireless LAN and Bluetooth 4.2/BLE. The enhancements retained are improved USB mass-storage booting and improved thermal management. The entire Raspberry Pi 3 Model A+ board is an FCC certified radio module. This will significantly reduce the cost in conformance testing Raspberry Pi–based products. What is shrunk is the price which is now down to $25 and the board size of 65x56mm, the size of a HAT. Source: Raspberry website Raspberry Pi 3 Model A+ will likely be the last product for now In March this year, Raspberry said that the 3+ platform is the final iteration of the “classic” Raspberry Pi boards. The next steps/released products will be out of necessity and not an evolution. This is because for an evolution to happen Raspberry will need a new core silicon, on a new process node, with new memory technology. So this new board, the 3A+ is about closing things; meaning we won’t see any more products in this line, in the foreseeable future. This board does answer one of their most frequent customer requests for ‘missing products’. And clears their pipeline to focus on building the next generation of Raspberry Pi boards. For more details visit the Raspberry Pi website. Introducing Raspberry Pi TV HAT, a new addon that lets you stream live TV Tensorflow 1.9 now officially supports Raspberry Pi bringing machine learning to DIY enthusiasts Should you go with Arduino Uno or Raspberry Pi 3 for your next IoT project?

The WoA Installer for Raspberry Pi 3 enables installing Windows 10 on the credit card size computer. The WoA Installer for Raspberry Pi 3 is made by the same members who brought Windows 10 ARM to the Lumia 950 and 950 XL. Where to start? To get started, you need Raspberry Pi 3 Model B or B+, a microSD card of at least class 1, and a Windows 10 ARM64 Image which you can get from GitHub. You also need a recent version of Windows 10 and .NET Framework 4.6.1. The WoA Installer is just a tool which helps you to deploy Windows 10 on the Raspberry Pi 3. WoA Installer needs the Core Package in order to run. You can find them listed on the GitHub page. Specification comparison Regarding specifications, the minimum requirements for Windows 10 is: Processor: 1 gigahertz (GHz) or faster processor or SoC. RAM: 1 gigabyte (GB) for 32-bit or 2 GB for 64-bit. Hard disk space: 16 GB for 32-bit OS 20 GB for 64-bit OS The Raspberry Pi 3B+ has specifications just good enough to run Windows 10: SoC: Broadcom BCM2837B0 quad-core A53 (ARMv8) 64-bit @ 1.4GHz RAM: 1GB LPDDR2 SDRAM While this sounds good, a Hacker news user points out: “Caution: To do this you need to run a rat's nest of a batch file that runs a bunch of different code obtained from the web. If you're going to try this, try on devices you don't care about. Or spend innumerable hours auditing code. Pass -- for now.” You can check out the GitHub page for more instructions. Raspberry Pi opens its first offline store in England Introducing Strato Pi: An industrial Raspberry Pi Raspberry Pi launches it last board for the foreseeable future: the Raspberry Pi 3 Model A+ available now at $25

An open-source libre GPU project is under the works by Luke Kenneth Casson Leighton. He is the hardware engineer who developed the EOMA68, an earth-friendly computer. The project already has access to $250k USD in funding. The basic idea for this "libre GPU" is to use a RISC-V processor. The GPU will be mostly software-based. It will leverage the LLVM compiler infrastructure and utilize a software-based Vulkan renderer to emit code and run on the RISC-V processor. The Vulkan implementation will be used for writing in the Rust programming language. The project's current road-map has details only on the software side of figuring out the RISC-V LLVM back-end state. Work is being done on writing a user-space graphics driver, implementing the necessary bits for the proposed RISC-V extensions like "Simple-V". While doing this, they will start figuring out the hardware design and the rest of the project. The road-map is quite simplified for the arduous task at hand. The website notes: “Once you've been through the "Extension Proposal Process" with Simple-V, it need never be done again, not for one single parallel / vector / SIMD instruction, ever again.” This process will include creating a fixed-function 3D "FP to ARGB" custom instruction, a custom extension with special 3D pipelines. With Simple-V, there is no need to worry about about how those operations would be parallelised. This is not a new concept, it's borrowed directly from videocore-iv. videocore-iv calls it "virtual parallelism". It's an enormous effort on both the software and hardware ends to come up with a RISC-V, Rust, LLVM, and Vulkan open-source combined project. It is difficult even with the funding considering it is a software based GPU. It is worth noting that the EOMA68 project was started by Luke in 2016 and raised over $227k USD from crowdfunding participants and hasn't shipped yet. To know more about this project, visit the libre risc-v website. NVIDIA leads the AI hardware race. But which of its GPUs should you use for deep learning? AMD ROCm GPUs now support TensorFlow v1.8, a major milestone for AMD’s deep learning plans PyTorch-based HyperLearn Statsmodels aims to implement a faster and leaner GPU Sklearn

Launched in 2018, Alibaba’s chip subsidiary, Pingtouge made a major announcement yesterday. Pingtouge is launching its first product - chip processor XuanTie 910 using the open-source RISC-V instruction set architecture. The XuanTie 910 processor is expected to reduce the costs of related chip production by more than 50%, reports Caixin Global. XuanTie 910, also known as T-Head, will soon be available in the market for commercial use. Pingtouge will also be releasing some of XuanTie 910’s codes on Github for free to help the global developer community to create innovative applications. No release dates have been revealed yet. What are the properties of the XuanTie 910 processor? The XuanTie 910 16-core processor has 7.1 Coremark/MHz and its main frequency can achieve 2.5GHz. This processor can be used to manufacture high-end edge-based microcontrollers (MCUs), CPUs, and systems-on-chip (SOC). It can be used in applications like 5G telecommunication, artificial intelligence (AI), and autonomous driving. XuanTie 910 processor gives 40% increased performance over the mainstream RISC-V instructions and also a 20% increase in terms of instructions. According to Synced, Xuantie 910 has two unconventional properties: It has a 2-stage pipelined out-of-order triple issue processor with two memory accesses per cycle. The processors computing, storage and multi-core capabilities are superior due to an increased extension of instructions. Xuantie 910 can extend more than 50 instructions than RISC-V. Last month, The Verge reported that an internal ARM memo has instructed its staff to stop working with Huawei. With the US blacklisting China’s telecom giant Huawei, and also banning any American company from doing business with them, it seems that ARM is also following the American strategy. Although ARM is based in U.K. and is owned by the Japanese SoftBank group, it does have an “US origin technology”, as claimed in the internal memo. This may be one of the reasons why Alibaba is increasing its efforts in developing RISC-V, so that Chinese tech companies can become independent from Western technologies. A Xuantie 910 processor can assure Chinese companies of a stable future, with no fear of it being banned by Western governments. Other than being cost-effective, RISC-V also has other advantages like more flexibility compared to ARM. With complex licence policies and high power prospect, it is going to be a challenge for ARM to compete against RISC-V and MIPS (Microprocessor without Interlocked Pipeline Stages) processors. A Hacker News user comments, “I feel like we (USA) are forcing China on a path that will make them more competitive long term.” Another user says, “China is going to be key here. It's not just a normal market - China may see this as essential to its ability to develop its technology. It's Made in China 2025 policy. That's taken on new urgency as the west has started cutting China off from western tech - so it may be normal companies wanting some insurance in case intel / arm cut them off (trade disputes etc) AND the govt itself wanting to product its industrial base from cutoff during trade disputes” Some users also feel that it is technology that wins when two big economies continue bringing up innovative technologies. A comment on Hacker News reads, “Good to see development from any country. Obviously they have enough reason to do it. Just consider sanctions. They also have to protect their own market. Anyone that can afford it, should do it. Ultimately it is a good thing from technology perspective.” Not all US tech companies are wary of partnering with Chinese counterparts. Two days ago, Salesforce, an American cloud-based software company announced a strategic partnership with Alibaba. This aims to help Salesforce localize their products in mainland China, Hong Kong, Macau, and Taiwan. This will enable Salesforce customers to market, sell, and operate through services like Alibaba Cloud and Tmall. Winnti Malware: Chinese hacker group attacks major German corporations for years, German public media investigation reveals The US Justice Department opens a broad antitrust review case against tech giants Salesforce is buying Tableau in a $15.7 billion all-stock deal

Yesterday the Raspberry Pi Foundation launched a new device called the Raspberry Pi TV HAT. It is a small board, TV antenna that lets you decode and stream live TV. The TV HAT is roughly the size of a Raspberry Pi Zero board. It connects to the Raspberry Pi via a GPIO connector and has a port for a TV antenna connector. The new Raspberry Pi addon is designed after a new form factor of HAT (Hardware Attached on Top). The addon itself is a half-sized HAT matching the outline of Raspberry Pi Zero boards. Source: Raspberry Pi website TV HAT specifications and requirement The board addon has a Sony CXD2880 TV tuner. It supports TV standards like DVB-T2 (1.7MHz, 5MHz, 6MHz, 7MHz, 8MHz channel bandwidth), and DVB-T (5MHz, 6MHz, 7MHz, 8MHz channel bandwidth). The frequencies it can recieve are VHF III, UHF IV, and UHF V. Raspbian Stretch (or later) is required for using the Raspberry Pi TV HAT. TVHeadend is the recommended software to start with TV streams. There is a ‘Getting Started’ guide on the Raspberry Pi website. Watch on the Raspberry Pi With the TV HAT can receive and you can view television on a Raspberry Pi board. The Pi can also be used as a server to stream television over a network to other devices. When running as a server the TV HAT works with all 40-pin GPIO Raspberry Pi boards. Watching on TV on the Pi itself needs more processing, so the use of a Pi 2, 3, or 3B+ is recommended. The TV HAT connected to a Raspberry Pi board: Source: Raspberry Pi website Streaming over a network Connecting a TV HAT to your network allows viewing streams on any device connected to the network. This includes computers, smartphones, and tablets. Initially, it will be available only in Europe. The Raspberry Pi TV HAT is now on sale for $21.50, visit the Raspberry Pi website for more details. Tensorflow 1.9 now officially supports Raspberry Pi bringing machine learning to DIY enthusiasts How to secure your Raspberry Pi board [Tutorial] Should you go with Arduino Uno or Raspberry Pi 3 for your next IoT project?

Rigetti computers are committed to building the world’s most powerful computers and they believe the true value of quantum will be unlocked by practical applications. Rigetti CEO Chad Rigetti, posted recently on Medium about their plans to deploy 128 qubit chip quantum computing system, challenging Google, IBM, and Intel for leadership in this emerging technology. They have planned to deploy this system in the next 12 months and shared their investment in resources at the application layer to encourage experimentation on quantum computers. Over the past year, Rigetti has built 8-qubit and 19-qubit superconducting quantum processors, which are accessible to users over the cloud through their open source software platform Forest. These chips have been useful in helping researchers around the globe to carry out and test programs on their quantum-classical hybrid computers. However, to drive practical use of quantum computing today, Rigetti must be able to scale and improve the performance of the chips and connect them to the electronics on which they run . To achieve this, the next phase of quantum computing will require more power at the hardware level to drive better results. Rigetti is in a unique position to solve this problem and build systems that scale. Chad Rigetti adds, “Our 128-qubit chip is developed on a new form factor that lends itself to rapid scaling. Because our in-house design, fab, software, and applications teams work closely together, we’re able to iterate and deploy new systems quickly. Our custom control electronics are designed specifically for hybrid quantum-classical computers, and we have begun integrating a 3D signaling architecture that will allow for truly scalable quantum chips. Over the next year, we’ll put these pieces together to bring more power to researchers and developers.” While they are focussed on building the 128 qubit chip, the Rigetti team is also looking at ways to enhance the application layer by pursuing quantum advantage in three areas; i.e. quantum simulation, optimization and machine learning. The team believes quantum advantage will be achieved by creating a solution that is faster, cheaper and of a better quality. They have posed an open question as to which industry will build the first commercially useful application to add tremendous value to researchers and businesses around the world. Read the full coverage on the Rigetti Medium post. Quantum Computing is poised to take a quantum leap with industries and governments on its side Q# 101: Getting to know the basics of Microsoft’s new quantum computing language PyCon US 2018 Highlights: Quantum computing, blockchains and serverless rule!

Italian companies have designed Strato Pi, a Raspberry Pi based board intended to be used in industrial applications. It can be used in areas where a higher level of reliability is required. Source: sferlabs website Strato Pi features The board is roughly the same size of Regular Raspberry Pi 2/3 and is engineered to work in an industrial environment that demands more rugged devices. Power supply that can handle harsh environments The Strato Pi can accept a power supply from a wide range and can handle substantial amounts of ripple, noise and voltage fluctuations. The power supply circuit is heavily protected and filtered with oversized electrolytic capacitors, diodes, inductors, and a high efficiency voltage regulator. The power converter is based on PWN converted integrated circuits which can provide up to 95% power efficiency and up to 3A continuous current output. Over current limiting, over voltage protection and thermal shutdown are also built-in. The board is also protected against reverse polarity with resettable fuses. There is surge protection up to ±500V/2ohms 1.2/50μs which ensures reliability even in harsh environments. UPS to safeguard against power failure In database and data collection applications, supper power interruption may cause data loss. To tackle this Strato Pi has an integrated power supply that gives enough time to save data and shutdown when there is a power failure. The battery power supply stage of the board supplies power to the Strato Pi circuits without any interruption even when the main power supply fails. This stage also charges the battery via a high efficiency step-up converter to generate the optimal charging voltage independent of the main power supply voltage value. Built-in real time clock The Strato Pi has a built-in battery-backed real time clock/calendar. It is directly connected to the Raspberry Pi via the I2C bus interface. This shows the correct time even when there is no internet connection. This real time clock is based on the MCP79410 general purpose Microchip RTCC chip. A replaceable CR1025 battery acts as backup power source when the main power is not available. In always powered on state, the battery can last over 10 years. Serial Port Strato Pi uses the interface circuits of the RS-232 and RS-485 serial ports. They are insulated from the main and battery power supply voltages which avoids failures due to ground loops. A proprietary algorithm powered micro-controller, automatically manages the data direction of RS-485. Without any special configuration, the baud rate and the number of bits are taken into account. Thus, the Raspberry board can communicate through its TX/RX lines without any other additional signal. Can Bus The Controller Area Network (CAN) bus is widely used and is based on a multi-master architecture. This board implements an easy to use CAN bus controller. It has both RS-485 and CAN bus ports which can be used at the same time. CAN specification version 2.0B can be used and support of up to 1 Mbps is available. A hardware watchdog A hardware watchdog is an electronic circuit that can automatically reset the processor if there is a software hang. This is implemented with the help of the on board microcontroller. This is independent of  the Raspberry Pi’s internal CPU watchdog circuit. The base variant starts at roughly $88. They also have a mini and products like a prebuilt server. For more details on Strato Pi, sferlabs website. Raspberry Pi launches it last board for the foreseeable future: the Raspberry Pi 3 Model A+ available now at $25 Introducing Raspberry Pi TV HAT, a new addon that lets you stream live TV Intelligent mobile projects with TensorFlow: Build your first Reinforcement Learning model on Raspberry Pi [Tutorial]

After a long wait, Intel has officially launched its first 10th generation core processors, code-named ‘Ice Lake’. The first batch contains 11 highly integrated 10nm processors which showcases high-performance artificial intelligence (AI) features and is designed for sleek 2 in 1s and laptops. The ‘Ice Lake’ processors are manufactured on Intel’s 10nm processor and consist of the 14nm chipset in the same carrier. It includes two or four Sunny Cove cores along with Intel’s Gen 11 Graphics processing unit (GPU). The 10nm measure of the processor indicates the size of the transistors used. The 10 nanometer miniscule length also shows the power of the transistor as it is considered that smaller the transistor, better is its power consumption. Read More: Intel unveils the first 3D Logic Chip packaging technology, ‘Foveros’, powering its new 10nm chips, ‘Sunny Cove’ Chris Walker, Intel corporate vice president and general manager of Mobility Client Platforms in the Client Computing Group says that “With broad-scale AI for the first time on PCs, an all-new graphics architecture, best-in-class Wi-Fi 6 (Gig+) and Thunderbolt 3 – all integrated onto the SoC, thanks to Intel’s 10nm process technology and architecture design – we’re opening the door to an entirely new range of experiences and innovations for the laptop.” Intel was supposed to ship the 10nm processors, way back in 2016. Intel CEO Bob Swan says that the delay was due to the “company’s overly aggressive strategy for moving to its next node.” Intel has also introduced a new processor number naming structure for the 10th generation ‘Ice Lake’ processors which indicates the generation and the level of graphics performance of the processor. Image source: Intel What’s new in the 10th generation Intel core processors? Intelligent performance The 10th generation core processors are the first purpose-built processors for AI on laptops and 2 in 1s. They are built for modern AI-infused applications and contains many features such as: Intel Deep Learning Boost, used for specifically boosting flexibility to run complex AI workloads. It has a dedicated instruction set that accelerates neural networks on the CPU for maximum responsiveness. Up to 1 teraflop of GPU engine compute for sustained high-throughput inference applications Intel’s Gaussian & Neural Accelerator (GNA) provides an exclusive engine for background workloads such as voice processing and noise prevention at ultra-low power, for utmost battery life. New graphics With the Iris Plus graphics, the 10th generation core processors imparts double graphic performance in 1080p and higher-level content creation in 4K video editing, application of video filters and high-resolution photo processing. This is the first time that Intel’s Graphics processing unit (GPU) will support VESA’s Adaptive Sync* display standard. It enables a smoother gaming experience across games like Dirt Rally 2.0* and Fortnite*. According to Intel, this is the industry's first integrated GPU to incorporate variable rate shading for better rendering performance, as it uses the Gen11 graphics architecture.  The 10th generation core processors supports the BT.2020* specification, hence it is possible to view a 4K HDR video in a billion colors. Best connectivity With improved board integration, PC manufacturers can innovate on form factor for sleeker designs with Wi-Fi 6 (Gig+) connectivity and up to four Thunderbolt 3 ports. Intel claims this is the “fastest and most versatile USB-C connector available.” In the first batch of 11 'Ice Lake' processors, there are 6 Ice Lake U series and 5 Ice Lake Y series processors. Given below is the complete Ice Lake processors list. Image Source: Intel Intel has revealed that laptops with the 10th generation core processors can be expected in the holiday season this year. The post also states that they will soon release additional products in the 10th generation Intel core mobile processor family due to increased needs in computing. The upcoming processors will “deliver increased productivity and performance scaling for demanding, multithreaded workloads.”   Users love the new 10th generation core processor features and are especially excited about the Gen 11 graphics. https://twitter.com/Tribesigns/status/1133284822548279296 https://twitter.com/Isaacraft123/status/1156982456408596481 Many users are also expecting to see the new processors in the upcoming Mac notebooks. https://twitter.com/ChernSchwinn1/status/1157297037336928256 https://twitter.com/matthewmspace/status/1157295582844575744 Head over to the Intel newsroom page for more details. Apple advanced talks with Intel to buy its smartphone modem chip business for $1 billion, reports WSJ Why Intel is betting on BFLOAT16 to be a game changer for deep learning training? Hint: Range trumps Precision. Intel’s new brain inspired neuromorphic AI chip contains 8 million neurons, processes data 1K times faster

Listening to the Arduino developer community, the Arduino team has released a command line interface (CLI) for it. The CLI is a single binary file that performs most of the features present in the IDE. There was a wide gap between using the IDE and being able to use CLI completely for everything in Arduino. The CLI will allow you to Install new libraries, create new projects, and compile projects directly from the command line. Developers will get an advantage to test their projects quickly. You can also create your own libraries and compile them directly, for your own or third-party codes. Installing project dependencies will be as easy as typing the following command: arduino-cli lib install "WiFi101” “WiFi101OTA” In addition, the CLI has a JSON interface added for easy parsing by other programs. There were many requests for makefiles integration and the support has been added for it. The Arduino CLI can run on both ARM and Intel (x86, x86_64) architectures which means it can be installed on a Raspberry Pi or on any server. Massimo Banzi, Arduino founder stated: “I think it is very exciting for Arduino, one single binary that does all the complicated things in the Arduino IDE.” The Arduino team looks forward to people seeing integrating this tool in various IDEs. In the blog post by the Arduino team they have mentioned, “Imagine having the Arduino IDE or Arduino Create Editor speaking directly to Arduino CLI – and you having full control of it. You will be able to compile on your machine or on our online servers, detect your board or create your own IDE on top of it!” CLI is a better alternative to PlatformIO and will work on all three major operating systems, Linux, Windows, and macOS. The code is open source but you will need a license for commercial use. Visit the GitHub repository to get started with Arduino CLI. How to assemble a DIY selfie drone with Arduino and ESP8266 How to build an Arduino based ‘follow me’ drone Should you go with Arduino Uno or Raspberry Pi 3 for your next IoT project?

The Raspberry Pi board developers can now make use of the latest TensorFlow 1.9 features to build their board projects. Most developers use Raspberry Pi for shaping their innovative DIY projects. The Pi also acts as a pathway to introduce people to programming with an added benefit of coding in Python. The main objective of blending TensorFlow with the Raspberry Pi board is to let people explore the capabilities of machine learning on cost-effective and flexible devices. Eben Upton, the founder of the Raspberry Pi project, says, “It is vital that a modern computing education covers both fundamentals and forward-looking topics. With this in mind, we’re very excited to be working with Google to bring TensorFlow machine learning to the Raspberry Pi platform. We’re looking forward to seeing what fun applications kids (of all ages) create with it.” By being able to use TensorFlow features, existing users, as well as new users, can try their hand on live machine learning projects. Here are few real-life examples of Tensorflow on Raspberry Pi: DonkeyCar platform DonkeyCar, a platform to build DIY Robocars, uses TensorFlow and the Raspberry Pi to create self-driving toy cars. Object Recognition Robot The Tensorflow framework is useful for recognizing objects. This robot uses a library, a camera, and a Raspberry Pi, using which one can detect up to 20,000 different objects. Waste sorting robot This robot is capable of sorting every piece of garbage with the same precision as a human. This robot is able to recognize at least four types of waste. To identify the category to which it belongs, the system uses TensorFlow and OpenCV. One can easily install Tensorflow from the pre-built binaries using Python pip package system from the pre-built binaries. One can also install it by simply running these commands on the Raspbian 9 (stretch) terminal: sudo apt install libatlas-base-dev pip3 install tensorflow Read more about this project on GitHub page 5 DIY IoT projects you can build under $50 Build your first Raspberry Pi project How to mine bitcoin with your Raspberry Pi

On Monday, Intel Labs introduced first of its kind cryogenic control chip codenamed Horse Ridge. According to Intel, Horse Ridge will enable commercially viable quantum computers and speed up development of full-stack quantum computing systems. Intel announced that Horse Ridge will enable control of multiple quantum bits (qubits) and set a clear path toward scaling larger systems. This seems to be a major milestone on the path to quantum practicality. As right now the challenge for quantum computing is that it only works at near-freezing temperatures. Intel is trying to change that with this control chip. As per Intel, Horse Ridge will be able to enable control at very low temperatures, as it will eliminate hundreds of wires going into a refrigerated case that houses the quantum computer. Horse Ridge is developed in partnership with Intel’s research collaborators at QuTech at Delft University of Technology. It is fabricated using Intel’s 22-nanometer FinFET manufacturing technology. The in-house fabrication of these control chips at Intel will dramatically accelerate the company’s ability to design, test, and optimize a commercially viable quantum computer, the company said. “A lot of research has gone into qubits, which can do simultaneous calculations. But Intel saw that controlling the qubits created another big challenge to developing large-scale commercial quantum systems,” states Jim Clarke, Director of quantum hardware, Intel in the official press release . “It’s pretty unique in the community, as we’re going to take all these racks of electronics you see in a university lab and miniaturize that with our 22-nanometer technology and put it inside of a fridge,” added Clarke. “And so we’re starting to control our qubits very locally without having a lot of complex wires for cooling.” The name “Horse Ridge” is inspired from one of the coldest regions in Oregon known as the Horse Ridge. It is designed to operate at cryogenic temperatures, approx 4 degrees Kelvin which is 7 degrees Fahrenheit and 4 degrees Celsius. What is the innovation behind Horse Ridge Quantum computers promise the potential to tackle problems that conventional computers can’t handle by themselves. Quantum computers leverage a phenomenon of quantum physics that allows qubits to exist in multiple states simultaneously. As a result, qubits can conduct a large number of calculations at the same time dramatically speeding up complex problem-solving. But Intel acknowledges the fact that the quantum research community still lags behind in demonstrating quantum practicality, a benchmark to determine if a quantum system can deliver game-changing performance to solve real-world problems. Till date, researchers have focused on building small-scale quantum systems to demonstrate the potential of quantum devices. In these efforts, researchers have relied upon existing electronic tools and high-performance computing rack-scale instruments to connect the quantum system to the traditional computational devices that regulates qubit performance and programs the system inside the cryogenic refrigerator. These devices are often custom designed to control individual qubits, requiring hundreds of connective wires in and out of the refrigerator. However, this extensive control cabling for each qubit hinders the ability to scale the quantum system to the hundreds or thousands of qubits required to demonstrate quantum practicality, not to mention the millions of qubits required for a commercially viable quantum solution. With Horse Ridge, Intel radically simplifies the control electronics required to operate a quantum system. Replacing these bulky instruments with a highly integrated system-on-chip (SoC) will simplify system design and allow for sophisticated signal processing techniques to accelerate set-up time, improve qubit performance, and enable the system to efficiently scale to larger qubit counts. “One option is to run the control electronics at room temperature and run coax cables down to configure the qubits. But you can immediately see that you’re going to run into a scaling problem because you get to hundreds or thousands of cables and it’s not going to work,” said Richard Uhlig, Managing Director Intel Labs. “What we’ve done with Horse Ridge is that it’s able to run at temperatures that are much closer to the qubits themselves. It runs at about 4 degrees Kelvin. The innovation is that we solved the challenges around getting CMOS to run at those temperatures and still have a lot of flexibility in how the qubits are controlled and configured.” To know more about this exciting news, check out the official announcement from Intel. Are we entering the quantum computing era? Google’s Sycamore achieves ‘quantum supremacy’ while IBM refutes the claim The US to invest over $1B in quantum computing, President Trump signs a law Quantum computing, edge analytics, and meta learning: key trends in data science and big data in 2019

The Siggraph 2018 Conference brought in the biggest announcements from Nvidia unveiling a new turing architecture and three new pro-oriented workstation graphics cards in its Quadro family. This is the greatest leap for Nvidia since the introduction of the CUDA GPU in 2006. The Turing architecture features new RT Cores to accelerate ray tracing and new Tensor Cores for AI inferencing to enable real-time ray tracing. The two engines along with more powerful compute for simulation and enhanced rasterization will usher in a new generation of hybrid rendering to address the $250 billion visual effects industry. Hybrid rendering enables cinematic-quality interactive experience, amazing new effects powered by neural networks and fluid interactivity on highly complex models. The company also unveiled its initial Turing-based products - the NVIDIA® Quadro® RTX™ 8000, Quadro RTX 6000 and Quadro RTX 5000 GPUs. They are expected to revolutionize the work of approximately 50 million designers and artists across multiple industries. At the Annual Siggraph conference, Jensen Huang, founder and CEO, Nvidia mentions, “Turing is NVIDIA’s most important innovation in computer graphics in more than a decade. Hybrid rendering will change the industry, opening up amazing possibilities that enhance our lives with more beautiful designs, richer entertainment and more interactive experiences. The arrival of real-time ray tracing is the Holy Grail of our industry.” Here’s the list of Turing architecture features in detail. Real-Time Ray Tracing Accelerated by RT Cores The Turing architecture is armed with dedicated ray-tracing processors called RT Cores. It will accelerate the computation similar to light and sound travel in 3D environments at up to 10 GigaRays a second. Turing will accelerate real-time ray tracing operations by up to 25x than that of the previous Pascal generation. GPU nodes can be used for final-frame rendering for film effects at more than 30x the speed of CPU nodes. AI Accelerated by powerful Tensor Cores The Turing architecture also features Tensor Cores, processors that accelerate deep learning training and inferencing, providing up to 500 trillion tensor operations a second. It will power AI-enhanced features for creating applications with new capabilities including DLAA (deep learning anti-aliasing). DLAA is a breakthrough in high-quality motion image generation for denoising, resolution scaling and video re-timing. These features are part of the NVIDIA NGX™ software development kit, a new deep learning-powered technology stack. It will enable developers to easily integrate accelerated, enhanced graphics, photo imaging and video processing into applications with pre-trained networks Faster Simulation and Rasterization with New Turing Streaming Multiprocessor A new streaming multiprocessor architecture is featured in the new Turing-based GPUs to add an integer execution unit, that will execute in parallel with the floating point datapath. A new unified cache architecture with double bandwidth of the previous generation is added too. As it is combined with new graphics technologies like variable rate shading, the Turing SM achieves unprecedented levels of performance per core. With up to 4,608 CUDA cores, Turing supports up to 16 trillion floating point operations in parallel with 16 trillion integer operations per second. Developers will be able to take advantage of NVIDIA’s CUDA 10, FleX and PhysX SDKs to create complex simulations, such as particles or fluid dynamics for scientific visualization, virtual environment and special effects. The new Turing architecture has already received support from companies like Adobe, Pixar, Siemens, Black Magic, Weta Digital, Epic Games and Autodesk. The new Quadro RTX is priced at $2,300 for a 16GB version and $6,300 for 24GB version. Double the memory to 48GB and Nvidia expects you to pay about $10,000 for the high-end card. For more information you may visit the Nvidia official blog page. IoT project: Design a Multi-Robot Cooperation model with Swarm Intelligence [Tutorial] Amazon Echo vs Google Home: Next-gen IoT war 5 DIY IoT projects you can build under $50

Nvidia Tesla V100 GPUs are now publicly available in beta on Google Compute Engine and Kubernetes Engine. Also, Nvidia Tesla P100 GPUs are now generally available. Nvidia Tesla V100 GPU is almost equal to 100 CPUs. This gives customers more power to handle computationally demanding applications, like machine learning, analytics, and video processing. One can select as many as eight NVIDIA Tesla V100 GPUs, 96 vCPU and 624GB of system memory in a single VM, receiving up to 1 petaflop of mixed precision hardware acceleration performance. NVIDIA V100s are available immediately in the following regions: us-west1, us-central1 and europe-west4. Each V100 GPU is priced as low as $2.48 per hour for on-demand VMs and $1.24 per hour for Preemptible VMs. Making Nvidia Tesla V100 available on the compute engine is part of Google’s GPU expansion strategy. Similar to Google GPUs, the V100 is also billed by the second and Sustained Use Discounts apply. NVIDIA Tesla P100 GPU, on the other hand is a good fit if one wants a balance between price and performance. One can select up to four P100 GPUs, 96 vCPUs and 624GB of memory per virtual machine. The P100 is also now available in europe-west4 (Netherlands) in addition to us-west1, us-central1, us-east1, europe-west1 and asia-east1. * Maximum vCPU count and system memory limit on the instance might be smaller depending on the zone or the number of GPUs selected. ** GPU prices listed as hourly rate, per GPU attached to a VM that are billed by the second. Pricing for attaching GPUs to preemptible VMs is different from pricing for attaching GPUs to non-preemptible VMs. Prices listed are for US regions. Prices for other regions may be different. Additional Sustained Use Discounts of up to 30% apply to GPU on-demand usage only. Google Cloud makes managing GPU workloads easy for both VMs and containers by providing, Google Compute Engine where customers can use instance templates and managed instance groups to easily create and scale GPU infrastructure. NVIDIA V100s and other GPU offerings in Kubernetes Engine, where Cluster Autoscaler helps provide flexibility by automatically creating nodes with GPUs, and scaling them down to zero when they are no longer in use. Preemptible GPUs for both Compute Engine managed instance groups and Kubernetes Engine’s Autoscaler optimizes the costs while simplifying infrastructure operations. Read more about both the GPUs in detail on the Google Research Blog and benefits of each on Nvidia V100 and Nvidia P100 blog post. Google announce the largest overhaul of their Cloud Speech-to-Text Google’s kaniko – An open-source build tool for Docker Images in Kubernetes, without a root access How machine learning as a service is transforming cloud  

Raspberry Pi has opened a retail brick and mortar store in Cambridge, England. The mini computer maker has always sold its products online and ships to many countries. This offline store is a first for the company. Located at the Grand Arcade shopping, the Raspberry Pi store was started yesterday. It is not just a boring store with Raspberry Pi boards. Their collection includes boards, full setups with monitors, keyboards and mouses for demo, books, mugs and even soft toys with Raspberry branding. You can see some of the pictures of the new store here: https://twitter.com/Raspberry_Pi/status/1093454153534398464 A user shared his observation of the store on HackerNews: “I had a minute to check it out over lunch - most of the floorspace is dedicated to demonstrating what the raspberry pi can do at a high level. They had stations for coding, gaming, sensors, etc. but only ~1/4th of the space was devoted to inventory. They have a decent selection of Pis, sensor kits, and accessories. Not everyone working there was technical. This is definitely aimed at the general public.” Raspberry Pi has a strong online community with people coming up with various DIY projects. But the community is limited to people who have a keen interest on. More stores like this will help familiarize more people with Raspberry Pi. With branded books, demos, and toys this store is aimed to popularize the mini computer. Introducing Strato Pi: An industrial Raspberry Pi Raspberry Pi launches it last board for the foreseeable future: the Raspberry Pi 3 Model A+ available now at $25 Introducing Raspberry Pi TV HAT, a new addon that lets you stream live TV