By Arduino Team
Now on Kickstarter, ArduECU is an IP69K-rated waterproof, rugged and impact-resistant electronic control unit (ECU) that enables your Arduino projects to withstand the elements and other harsh environments.
ArduECU is compatible with all 12V to 24V systems, and can be used in a wide range of applications such as vehicle diagnostics and control, stationary machines, remote monitoring, industrial automation, and agriculture to name just a few.
Based on an ATmega328, the ECU can be programmed with the Arduino IDE and also supports CoDeSys, meaning you can now configure your ArduECU with ladder logic, functional block, structured text, instruction list, or sequential function charts.
ArduECU comes in three models–one for basic projects, one for CAN bus vehicle and machine control applications, and another which converts an existing Arduino Uno into a weatherproof, custom-tailored ECU with an on-board prototyping area for your own creations and circuits. Each of these units will have expansion headers to leverage IoT and wireless capabilities, including Wi-Fi, Bluetooth, cellular and GPS, or to house future expansion shields with additional functionality at a later time.
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In today’s so-called smart home, you can dim the lights, order more toothpaste or tell the kids to go to bed simply by talking to a small WiFi-connected speaker, such as Amazon’s Echo or Google’s Home.
This voice-first market–combining voice with artificial intelligence (AI)–barely existed in 2014. This year, Voice Labs, a consultancy, expects 24.5 million appliances to be shipped.
Other big tech firms have their own plans: Apple is taking its Siri voice assistant beyond its mobile devices to PCs, cars, and the home; Baidu last month bought Raven, billed as China’s answer to Amazon’s Alexa intelligent personal assistant; and Samsung Electronics plans to incorporate Viv, its newly acquired virtual assistant, into its phones and home appliances.
But not everyone thinks the future of communicating with the Internet of Things needs to be vocal.
Facebook founder Mark Zuckerberg, for example, was working on Jarvis, his own voice-powered AI home automation, and found he preferred communicating by text because, he wrote, “mostly it feels less disturbing to people around me.”
And several major appliance makers have turned to a small Singapore firm, Unified Inbox, which offers a service that can handle ordinary text messages and pass them on to appliances.
With your home added to the contacts list on, say, WhatsApp, a quick text message can “start the coffee machine”; “turn on the vacuum cleaner at 5 p.m.”; or “preheat the oven to 200 degrees at 6.30 p.m.”
“Think of it as a universal translator between the languages that machines speak … and us humans,” said Toby Ruckert, a German former concert pianist and now Unified Inbox’s CEO.
The company is just a small player, funded by private investors, but Ruckert says its technology is patent-backed, has been several years in the making, and has customers that include half of the world’s smart appliance makers, such as Bosch.
Unified Inbox connects the devices on behalf of the manufacturer, while the consumer can add their appliance by messaging its serial number to a special user account or phone number. It so far supports more than 20 of the most popular messaging apps, as well SMS and Twitter, and controls appliances from ovens to kettles. Other home appliances being tested include locks, garage openers, window blinds, toasters and garden sprinklers, says Ruckert.
“People aren’t going to want a different interface for all the different appliances in their home,” says Jason Jameson, of IBM, which is pairing its Watson AI supercomputer with Unified Inbox to better understand user messages. They will this week demonstrate the service working with a Samsung Robot Cleaner.
“The common denominator is the smartphone, and even more common is the messaging app,” Jameson notes.
There’s another reason, Ruckert says, why more than half of the world’s smart appliance manufacturers have signed up.
They’re worried the big tech companies’ one-appliance-controls-all approach will relegate them to commodity players, connecting to Alexa or another dominant platform, or being cast aside if Amazon moves into making its own household appliances.
“Our customers are quite afraid of the likes of Amazon,” Ruckert said. “Having a Trojan horse in a customer’s home, like Echo, that they must integrate with to stay competitive is a nightmare for them.”
An Amazon spokesperson said the company was “excited by the early response by smart home device manufacturers and even more excited by the customer response,” but declined to speculate about future plans.
A spokesperson for Bosch said no single company can knit the Internet of Things together, so “there is a need to collaborate and establish ecosystems,” such as working with Unified Inbox.
Our favorite smart home technology from CES 2017.
Already the race is on to incorporate other services into these home hubs.
Amazon allows third parties to develop apps, or “skills,” for Alexa. It has more than 10,000 of these, with many added in just the past three months. Most are developed by firms using Amazon’s software toolkit, and range from telling jokes to ordering food.
And Amazon makes it easy for other hardware makers to incorporate Alexa into their appliances, increasing its reach.
Chinese device maker Lenovo has embedded Alexa in its speakers, while General Electric has it in a lamp– meaning users can control these devices by voice, and use them to order products from Amazon. LG Electronics and Huawei are also working on Alexa-enabled devices, Amazon said.
Text messaging, though, may yet break down those walls.
As Zuckerberg noted, the volume of text messages is growing much faster than the number of voice calls. “This suggests that future AI products cannot be solely focused on voice, and will need a private messaging interface as well,” he says.
Some companies are already looking further ahead, and doing away with the need for any human instruction–whether by voice or text–by making machines smarter at learning our habits and anticipating them.
LG, for example, is using deep learning to make its appliances understand and avoid objects in a room, or fill an ice tray based on a user’s cold drink habits.
At Unified Inbox, Ruckert looks ahead to being able to communicate not only with one’s own appliances but with machines elsewhere. Bosch executives in Singapore, for example, have demonstrated how a user could ask a smart CCTV camera how many people were in a particular room.
Ruckert is also working with Singapore’s Nanyang Polytechnic to send updates to family members or staff direct from hospital equipment attached to patients.
And smart appliance entrepreneur James Dyson said in a recent interview that the future lies in what he calls “highly intelligent automation.”
“For me, the future is making everything happen for you without you being particularly involved in it.”
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Cadence® Tensilica® Fusion F1 DSP is part of the latest Methods2Business (M2B) Wi-Fi HaLow™ MAC IP offering. The licensable IP targets SoCs designed for battery-powered sensor nodes used in smart home, smart city and industrial applications. M2B leveraged the Fusion F1 DSP both to implement the IEEE 802.11ah MAC firmware and to run value-added applications like voice trigger, audio identification and sensor fusion on a single DSP. Customers can benefit from a hardware-software solution achieving optimal balance of low-power consumption, performance and programmability.
A demonstration of the complete Wi-Fi HaLow MAC/PHY, including M2B’s IEEE 802.11ah MAC IP platform with the Fusion F1 DSP and Adapt-IP’s digital baseband, will be shown at the Cadence booth at Mobile World Congress 2017 in Barcelona.
“The new Wi-Fi HaLow standard promises to offer the ultra-low power consumption and security required to fuel the growth of the IoT market. By leveraging the Fusion F1 DSP along with our custom instruction set extensions, we were able to deliver on these promises in our Wi-Fi HaLow MAC IP products,” said Marleen Boonen, CEO of Methods2Business. “Our unique and scalable solution allows us to target the smaller battery-powered sensor nodes and the higher performance, compute-intensive access points — all using the same flexible DSP.”
The Fusion F1 DSP offers low-energy, high-performance control and signal processing ideally suited for the IoT/wearable markets. This highly configurable DSP is specifically designed to excel at DSP processing tasks like wake-on-voice, voice pre-processing, sensor fusion and narrowband connectivity, as well as traditional control-code tasks like communications stacks and RTOSs. All processing workloads are completed with ultra-low energy consumption in a very small footprint.
“Customers looking at serving the HaLow Wi-Fi market need a scalable platform to build ultra-low-power sensor nodes and higher performance access points”, said Larry Przywara, group director of marketing, audio/voice IP, Cadence. “The Fusion F1 DSP-based HaLow MAC IP solution not only provides scalability but additionally its flexibility offers differentiating features to customer IoT applications directly on the modem — saving power and expense.”
Cadence is a leading provider of intellectual property (IP) for system on chip (SoC) developers. Cadence design IP, verification IP, and Tensilica processor IP have been used to simplify the design and verification of thousands of SoCs across automotive, mobile, enterprise, internet of things (IoT), and consumer applications. Cadence IP plays a vital role in the company’s overarching system design enablement strategy, which is to provide a comprehensive set of tools, design content, and services for the development of innovative electronic systems.
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Northeastern University – Silicon Valley, a leader in experiential learning and a key member of Northeastern University’s Global Network, is collaborating with Cisco to develop and deliver curricula to establish a laboratory for skills training in the Internet of Things (IoT). The program was developed to address the $6 trillion demand for IoT skills and the need to upskill workers for the next phase of connectivity.
The joint program will combine hands-on, experiential learning opportunities with essential coursework to prepare today’s workers for the demands of IoT technology.
“We’re predicting that the next wave of technology will require a different skill set than that of traditional networking, computer science, software, hardware, network and data communication engineers, and Big Data professionals,” said PK Agarwal, regional dean and CEO of Northeastern University-Silicon Valley. “To meet the requirements of these careers, we are working with an industry leader that is dedicated to cultivating talent and leadership for the emerging IoT industry.”
“In collaborating with Northeastern University-Silicon Valley, we plan to build upon our past successes to create a financially sustainable and operationally scalable program for developing highly employable IoT professionals,” said Gary Coman, Learning Engineering and Business Development Director for Cisco Corporate Affairs.
The new program will leverage Northeastern’s robust research enterprise and Cisco’s leadership in technology to prepare students for the next chapter in an interconnected world—a world that requires a strong foundation in hardware and software, knowledge of the entire technology stack, data analysis and communications, and cybersecurity.
The program is designed for working professionals who already have a strong background in the foundational subjects required for future success in IoT. Courses will be delivered in a part-time, hybrid format to accommodate the schedules of busy professionals. Most of the lecture-oriented instruction will be offered online for easy access. However, because of the importance of the connected nature and collaborative spirit of IoT, cohorts will come together for lab work hack-a-thons, and other professional skills trainings and experiential opportunities.
Founded in 1898, Northeastern is a global, experiential, research university built on a tradition of engagement with the world, creating a distinctive approach to education and research.
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Synopsys, Inc. has announced the availability of the DesignWare ARC SEM110 and SEM120Dsecurity processors for low-power, embedded applications such as smart metering, NFC payment and embedded SIMs. The new ARC SEM processors with Synopsys SecureShield technology enable designers to protect systems against software, hardware and side-channel attacks as well as separate secure and non-secure functions as part of a Trusted Execution Environment (TEE). The ultra-low power, performance-efficient ARC SEM processors offer advanced security features such as uniform instruction timing and power randomization to obfuscate secure operations. In addition, the SecureShield Runtime Library manages the partitioning and isolation of containers within a TEE to ensure data is stored and processed in a safe environment. This combination of hardware and software features enables designers to create more secure system-on-chips (SoCs) for IoT and mobile applications.
ARC SEM Security Processors
The DesignWare ARC SEM Processors are based on the scalable, 32-bit ARCv2 instruction set architecture (ISA) and are optimized for area and power efficiency. The ARC SEM110 Processor integrates a wide range of security technologies and can be implemented in an SoC as either a standalone secure core or as a single core performing both secure and non-secure functions. The ARC SEM120D adds DSP functionality for applications such as sensor processing and voice identification in health care and IoT devices. Key features of the new processors include:
- Side-channel resistance with uniform instruction timing and timing/power randomization obfuscate secure operations from potential hackers
- An enhanced memory protection unit and SecureShield technology simplify development of a TEE
- Tamper-resistant pipeline with in-line instruction/data encryption and address scrambling, and data integrity checks protect against system attacks and IP theft
- Integrated watchdog timer detects system failures including tampering
- DSP instructions and unified MUL/MAC unit in the ARC SEM120D support applications requiring enhanced security and real-time processing
“High-profile security breaches are increasingly in global headlines, underscoring the need to ensure the right precautions are being taken during SoC design and test,” said Mats Nählinder, president of Riscure North America. “As a global security test lab and a market leader in side-channel test equipment, our business is to evaluate security solutions for their ability to protect against such malicious attacks targeting hardware and software. The DesignWare ARC SEM processors enable designers to add protection to their embedded devices against such attacks.”
ARC Software, Development Tools and Ecosystem
Synopsys’ embARC Open Software Platform provides software developers with online access to a comprehensive suite of free and open-source software, including security transport protocols. The platform includes the new SecureShield Runtime Library, which runs in the background and manages the partitioning and isolation of containers within a TEE.
Like all ARC processors, the ARC SEM processors are supported by a robust ecosystem of software and hardware development tools, including:
- MetaWare Development Toolkit that generates highly efficient code ideal for deeply embedded applications
- ARC nSIM fast instruction set simulator enables early software development
- ARC xCAM provides 100 percent cycle-accurate simulation for software optimization and system verification
In addition, support for Synopsys’ HAPS physical prototyping system enables early software development, hardware/software integration and system validation of ARC SEM processor-based designs.
The ARC SEM processors with SecureShield are part of Synopsys’ comprehensive portfolio of security IP solutions that include the Enhanced Security Package and CryptoPack options for ARC EM processors and the DesignWare Security IP solutions, which consist of a range of cryptography cores and software library, protocol accelerators, root of trust, platform security and content protection IP.
“As security threats become more prevalent in connected devices, having security built-in at the SoC level is critical to minimizing the risk of side-channel attacks, data breaches, IP theft and more,” said John Koeter, vice president of marketing for IP and prototyping at Synopsys. “By expanding our portfolio of IP solutions with the new ARC SEM processors, Synopsys enables designers to integrate the necessary security features into their embedded devices, while meeting the stringent power and area requirements of their chips.”
For more information, please visit www.synopsys.com.
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Synopsys, and Gridbee Communications announced that Gridbee has achieved first-pass silicon success for its integrated IoT secure wireless communications solutions using Synopsys’ silicon-proven DesignWare ARC EM6 Processor. Gridbee chose Synopsys’ ARC EM processor over competitive processor alternatives for its superior power- and performance-efficiency, ability to secure code with advanced memory protection capabilities and availability of a comprehensive suite of tools and open source software. ARC EM’s extensive configurability enabled Gridbee to create an optimized implementation that delivers the essential signal processing required for machines to efficiently and securely communicate with each other within stringent power and silicon area budgets.
“To meet the profitability and reliability targets of the industrial IoT market, it is imperative that we develop secure, power-efficient and reliable plug-and-play wireless communication solutions that are easy for our customers to deploy,” said Stephane Laurent, CBDO & co-founder at Gridbee Communications. “Synopsys’ ARC EM processors are highly configurable and offer extremely low power consumption. ARC’s hardware advantages, combined with the available security features and software development tools, enable us to deliver tailored solutions that address the evolving challenges of IoT communications.”
Gridbee Communications develops plug-and-play wireless communication technologies for smart devices in industrial environments, including system-on-chip (SoC) components and related software. With a simple USB connected case, Gridbee’s customers can easily implement machine intelligence through peer to peer communication and networked devices. The solution is based on medium and long range radio frequency technology (IEEE 802.15.4g SUN with both FSK and OFDM modulation) to enable communication with the machines’ network and a mesh network infrastructure and use very little power when not communicating. Gridbee’s solution includes all the hardware and software components necessary for securely connecting smart devices.
DesignWare ARC EM Processors are based on the scalable, 32-bit ARCv2 instruction set architecture (ISA) and are optimized for area and power efficiency. The ARC EM cores’ small size make them ideal for applications where power consumption and size must be kept to a minimum without compromising performance. Synopsys’ Enhanced Security Package for ARC EM processors enables designers to create a tamper-resistant, secure environment that protects their systems and software from evolving security threats such as IP theft and remote attacks. The ARC EM Family is supported by a robust ecosystem of development tools and software, including the MetaWare Development Toolkit and ARC EM Starter Kit. In addition, Synopsys’ embARC Open Software Platform gives ARC EM software developers online access to a comprehensive suite of free and open-source software that eases the development of code for IoT and other embedded applications.
“The growth in connected machines is requiring wireless solutions that help guarantee reliable, high-speed, secure communications, while meeting demanding energy and cost budgets,” said John Koeter, vice president of marketing for IP and prototyping at Synopsys. “Selecting Synopsys’ ARC processors with the MetaWare toolkit and embARC Open Software Platform enabled Gridbee to meet the performance and power efficiency of their design targets while accelerating the deployment of their innovative M2M solutions to the market.”
Learn more at www.gridbeecom.com.
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By Alex Woodie
Developers have been filing the rough edges off Apache Hadoop ever since the open source project started to gain traction in the enterprise. But if Hadoop is going to take the next step and become the backbone of analytics for businesses of all sizes—as many hope it will—then the platform needs to shed some of its technical complexity and embrace simplicity.
There are many ways to parse this call for greater simplicity (which, ironically, is itself a source of complexity). Needless to say, the complexity that appears to be keeping Hadoop back lies primarily in how organizations build, deploy, and manage their Hadoop stacks and the big data apps that run on them.
Reduce the Churn
Take the Hadoop distribution shipped by Hortonworks (NASDAQ: HDP) as an example. Over the past couple of years, HDP customers have been inundated with new technology at a furious pace. Every few months, they were given the option to adopt new releases of Hadoop ecosystem components like Spark, Storm, Hive, and Hbase on top of new releases of Apache Hadoop itself.
While the releases represented real innovation by the various open source projects that make up the Hadoop ecosystem, it proved too much to absorb nonetheless. It “created confusion for customers,” Hortonworks vice president of product management Tim Hall told Datanami.
In response, the company adopted a new release strategy earlier this month that lets customers get off the treadmill of seemingly never-ending upgrades parts. Customers that value stability in their Hadoop clusters above innovation can stick to major releases of Core HDP services (HDFS, MapReduce, and YARN), which will get upgraded once a year, per the Open Data Platform Initiative (ODPi).
HDP is composed of nearly two-dozen related Apache projects
Meanwhile, customers who will trade some stability for greater innovation can still get that with more frequent updates to Extended HDP services like Spark, Storm, Hive, and HBase. “It eliminates unnecessary upgrade churn where they feel they have to continually adopt the latest and greatest platform when they have a job to do,” Hall says. “People want to run their business.”
The ODPi was founded a year ago to simplify life for software vendors, and to streaming the testing and validation of the various parts that make up the Hadoop stack. What’s interesting is that Hortonworks realized that Hadoop customers had the same concerns. While open source innovation is the lifeblood of the Hadoop ecosystem, it is possible to have too much of a good thing.
Simplify the Menu
The same dynamic that led Hortonworks to pare down on the bit churn led Hadoop as a service provider Altiscale to today’s announcement of the Altiscale Insight Cloud, which essentially provides a superset of the best and most commonly requested Hadoop capabilities around data ingest, transformation, and analysis.
“About 80 percent of our customers and prospects were all doing the same thing on top of Hadoop,” says Altiscale founder and CEO Raymi Stata. “We saw this over and over again. But when you have something raw in Hadoop, there’s lots of different ways to tackle each and every one of those steps.”
Previously, Altiscale would explain–in great gory detail–all the possible ways customers could do things in Hadoop. “We proudly were saying ‘There’s five different interactive Hadoop engines. Here’s six ways to do ETL on Hadoop, and here’s the pros and cons of each,’” Stata says. “We stepped back and realized we weren’t necessarily doing customers a favor by giving them a long menu of pros and cons.”
Altiscale is offering customers a simpler menu of Hadoop options
The new Altiscale Insight Cloud represents the company’s efforts to simplify the options without compromising on functionality. The service is composed of a Web-based user interface that front-ends Oozie (for data ingestion) and Spark SQL (for analysis). Altiscale delivers some of its own basic transformation capabilities via the Web-based UI, but recommends customers write Spark scripts for more complex transformations. (Apache Spark, by itself, is a powerful force for simplification in Hadoop.)
Customers with complex Hadoop needs can still count on Altiscale to help them get it running. But Stata realized that for some customers, less is more.
“We have two classes of customers,” Stata says. “We have Hadoop veterans, people who have Hadoop in production…But increasingly we want to serve the greenfield customers, people who want to get into big data but they haven’t gotten there yet. How do we help them get business value more quickly?”
“I was personally slow to come to that realization,” Stata continues. “I was quite proud of the fact that we had great list of pros and cons and we were very impartial about it. But I realized that it is overwhelming. A lot of times, for newer customers, they’ve never used any of these systems. They can look at pros and cons, but they really didn’t appreciate what they meant and how they applied to their context.”
Data Format Normalization
Striving for simplicity in Hadoop runs counter to the momentum in the world around us. Data volumes are growing geometrically, and much of the data we’re storing in Hadoop is messy and lacks structure. It’s a veritable breeding ground for complexity.
Depending on what you want to do with the data (i.e. crunch it using a SQL engine, run machine learning algorithms, archive it), a Hadoop user may store the data in different data formats, such as Avro, Parquet, or optimized row column (ORC) formats. However, converting the formats consumes considerable amounts of time and CPU power, which are increasingly scarce commodities.
Recently, a new project called Apache Arrow sprung up to solve this problem by providing a standard data format for all in-memory columnar analytic engines running on Hadoop. The technology, which is being adopted by projects like Drill, Impala, Phoenix, and Spark, should speed up data access times by SQL-on-Hadoop engines by one to two orders of magnitude in part by leveraging the single instruction, multiple data (SIMD) capabilities of Intel processors.
Projects like Arrow show how the Hadoop community is responding to complexity by embracing industry standards. The work that went into Arrow is anything but simple, but the project itself actually promotes simplicity by shielding the rest of the industry from technical complexity. The next test is how developers will respond to the vast amounts of machine data emanating from the Internet of Things (IoT).
Hadoop was initially designed to crunch massive amounts of data in batch-oriented MapReduce jobs. The platform did that job well, but as the data volumes grew, customers increasingly wanted to ingest and analyze the data more quickly.
This has spurred the creation of separate big data architectures, such as Apache Kafka, designed to help companies manage the enormous volumes of real-time data. While many Hadoop distributors include Kafka in the binaries they ship to customers, Kafka doesn’t actually run on Hadoop. This has fueled the rise of separate data clusters for Hadoop and Kafka which (you guessed it) increases complexity.
Jay Kreps, who led the design of Kafka at LinkedIn, has told Datanami he hasn’t heard a good argument for making Kafka part of Hadoop proper, and that having separate clusters enables each to do its job in the most effective way. There are efforts to bring Kafka into the Hadoop realm (see: Project Koya), but for now the Confluent co-founder and CEO is resisting those efforts and maintaining a best-of-breed outlook.
With Kafka increasingly at the center of the real-time data universe, it’s led Hadoop distributors to adopt different strategies. Hortonworks’ answer to the IoT question is a new product called Hortonworks Data Flow (HDF). The HDF engine, which runs separately from Hadoop and front-ends Kafka, enables customers to ingest and analyze data at massive scale.
Another option is Kudu, the fast new in-memory storage engine unveiled by Cloudera last year. With Kudu, Cloudera aimed to split the difference between HDFS and HBase in an effort to “dramatically simplify” the increasingly convoluted Hadoop architectures used to support real time use cases, particularly those involving fast-moving machine data emanating from the IoT (where many organizations are employing Kafka). Kudu is currently incubating as an Apache project.
To the folks at MapR Technologies, these efforts just add to complexity. The big data software vendor addressed this complexity by creating a single platform that seeks to address these different needs. The HDFS and NFS-compatible file system, dubbed MapR-FS, is at the heart of the MapR Converged Platform, which houses the company’s Hadoop, NoSQL, and Kafka-compatible software.
Signs In the Clouds
The cloud and virtualization technologies are also poised to help reduce the complexity of Hadoop and to get big data benefits into people’s hands more quickly. Wikibon‘s big data analyst George Gilbert advocates that customers look to hosted Hadoop and big-data services, such as Amazon (NASDAQ: AMZM) Web Services, Google (NASDAQ: GOOG) Cloud Compute, or Microsoft (NASDAQ: MSFT) Azure to solve what he calls Hadoop’s “manageability challenge.”
“Hadoop already comes with significant administrative complexity by virtue of its multi-product design,” Gilbert notes. “On top of that, operating elastic applications couldn’t be more different from the client-server systems IT has operated for decades.”
For years, CIOs have been looking to virtualization tools like VMware to mask operational complexity, and this is another trend worth watching in the big data space. Look to Cloudera, which hired former Google Kubernetes guru Daniel Sturman last June to lead its engineering efforts, for potential breakthroughs here.
There’s also some work being done by folks like MapR, which is now supporting Docker containers with its unified big data distribution. Elsewhere, third-party vendors like BlueData and Denodo are making progress in the data virtualization space by enabling users to (among other things) quickly spin up Hadoop, Spark, Cassandra, and Kafka clusters atop hypervisors or virtualized containers, like Docker. Developers, meanwhile, are looking to frameworks like Cask and Concurrent to simplify programming on increasingly complex big data architectures.
There’s no way to completely avoid complexity within Hadoop or the applications that run on it. The technology and the data itself are moving too quickly for that. But by keeping simplicity one of the goals when designing Hadoop software or services, engineers will be able to mask some of the underlying complexity, thereby enabling regular folks to more easily interact with Hadoop-based systems, and that benefits everybody.
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Synopsys, and Intrinsic-ID announced the integration of Intrinsic-ID’s PUF technology with Synopsys’ DesignWare ARC EM Processors with SecureShield technology to enable efficient implementation of security functions such as authentication and device cloning prevention for low-power Internet-of-Things (IoT) edge devices. Intrinsic ID’s Quiddikey product is a secure key management solution based on their PUF technology that dynamically reconstructs on-chip secret keys without ever storing them, while Synopsys SecureShield technology provides a secure environment isolated from user code to protect the unclonable key. This combined solution enables system-on-a-chip (SoC) developers to support security-sensitive transactions, such as smart payment and secure cloud storage, found in applications including wearables and smart home appliances, without the cost or power consumption of a separate security processor core. SoC developers can add a complete security stack to low-power microprocessors and sensors without modifying any hardware.
“The rapid proliferation of connected devices and the new business models built on them have made secure user, device authentication and the management of valuable data critical,” said Pim Tuyls, CEO at Intrinsic-ID. “With the combination of Synopsys and Intrinsic-ID IP, designers can deploy a firmware-only implementation of our unique PUF technology in a trusted execution environment by leveraging Synopsys’ ARC EM processors with SecureShield. This solution enables the creation of highly secure, low-power SoCs that deliver superior anti-tamper and anti-cloning features for a wide range of IoT applications.”
Intrinsic-ID’s PUF security technology, called Hardware Intrinsic Security™ (HIS), uses a device-unique authentication process to extract security keys and unique identifiers from the innate characteristics of the SRAM. This extraction is done with Intrinsic-ID’s Quiddikey product. Quiddikey guarantees the entropy of the key as well as a correct and secure key reconstruction under all circumstances. The PUF key is extracted from the chip and not externally programmed or stored; it is linked to the chip’s unique physical characteristics and inherently protected against cloning and tampering.
DesignWare ARC EM Processors are based on the scalable, 32-bit ARCv2 instruction set architecture (ISA) and are optimized for area and power efficiency, making them ideally suited for IoT edge devices. Synopsys’ Enhanced Security Package with SecureShield technology provides the ability to encrypt instructions and data, enabling designers to create a tamper-resistant, secure environment that protects their systems and software from evolving security threats such as IP theft and remote attacks. Intrinsic-ID’s PUF solution can be implemented in firmware leveraging a trusted execution environment provided by Synopsys’ SecureShield, isolating critical security functions from the application software running on the ARC EM processor. The DesignWare CryptoPack option provides the ability to speed up software encryption implementations by adding custom instructions and registers to the ARC EM processors. This further accelerates the PUF and associated security algorithms to maximize performance and minimize power consumption when executing data authentication and encryption.
“As more personal data is being transmitted in connected systems and devices, consumers are becoming increasingly concerned about the privacy and security of their information,” said John Koeter, vice president of marketing for IP and prototyping at Synopsys. “Our collaboration with Intrinsic-ID provides designers with an advanced security solution that enables them to combine Intrinsic ID’s PUF solution and Synopsys’ ARC EM Processors with SecureShield technology for a faster, easier path to securing their IoT devices with strong authentication and the prevention of copying, cloning and other malicious attacks.”
Learn more at www.synopsys.com. (Source: www.prnewswire.com)
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