TensorFlow Pi Port Is Strategic Move in Edge Analytics Game

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August 10, 2018

Google’s recent announcement that it had ported its open source TensorFlow machine intelligence (ML) library for neural networking to the Raspberry Pi was the latest in a series of chess moves from Google and its chief AI rival Nvidia to win the hearts and keyboards of embedded Linux developers. The competition is a part of a wider battle with Amazon, Microsoft, Intel, and others to bring cloud analytics to the edge in IoT networks to reduce latency, increase reliability, and improve security.

Thanks to a collaboration with the Raspberry Pi Foundation, the latest TensorFlow 1.9 release can now be installed on Raspberry Pi 2 or 3 SBCs from pre-built binaries using Python’s pip package system. Raspbian 9 users can install it with two simple commands.

Integrating TensorFlow models into an embedded project offers further challenges. Yet, as Google has shown with its AIY Projects kits for the Raspberry Pi, you can add a bit of ML smarts in Raspberry Pi based robots, vision systems, and other embedded gear without a huge learning curve.

The TensorFlow port should be particularly welcome in the Raspberry Pi educational community. As the RPi Foundation’s Eben Upton wrote in a congratulatory tweet about the “massive news,” the TensorFlow port will enable “cool machine-learning educational content.”

TensorFlow was essentially born to run on Linux, but on servers or desktops, not on a modest SBC like the Raspberry Pi. It now runs on all major server and desktop platforms and has been ported to Android and iOS. Yet, the Raspberry Pi was a particularly gnarly challenge, writes Google TensorFlow developer Pete Warden in the announcement. It wasn’t even possible until the Raspberry Pi 2 and 3 came along with faster quad-core processors.

A year ago, Warden and his team managed to cross-compile TensorFlow on the RPi 3, but it was a slow, complicated, crash-prone process. The new ability to install from pre-built binaries now makes it feasible for a much wider group of developers to join the party.

While Google’s AIY Projects was attempting to squeeze a cloud-based platform onto a simple hacker board, its team started with low-cost cardboard constructed kits with add-on boards for connecting with Google Cloud related embedded technologies. These include the AIY Vision Kit for the Raspberry Pi Zero W and WH, which performs TensorFlow-based vision recognition. It incorporates a “VisionBonnet” board with an Intel Myriad 2 neural network accelerator chip. AIY Projects also launched an AIY Voice Kit with the same RPi Zero WH target that lets you build a voice-controlled speaker with Google Assistant support.

As noted in this Hackster.io post about the port from Alasdair Allan, the AIY Vision Kit has struggled to perform well when operating locally. The Voice Kit has done better due to its greater reliance on Google Cloud.

Google’s Edge TPU accelerator

According to a Warden tweet following the announcement, TensorFlow is not currently tapping the potential ML powers of Broadcom’s VideoCore graphical processing unit, as Nvidia does with its more powerful Pascal GPU. He goes on to suggest that there might be potential for developing a special GPU-related port for the single-core Raspberry Pi Zero boardlets, but for now there’s sufficient power on the Pi’s four CPU cores. Speaking of potential hooks to the GPU, he writes: “With quad-core CPUs and Neon on the latest Pi’s, there’s not as big an advantage, though it’s still interesting on Pi Zeroes.”

Another interpretation is that Google is skipping the GPU because it expects Raspberry Pi users and other embedded developers to tap its recently announced, Linux-friendly Edge TPU ML accelerator chip for TensorFlow. The Edge TPU will be offered this fall along with an NXP i.MX8M based Linux development kit and an Edge TPU Accelerator USB dongle that can fit into any Linux computer including the Pi.

The Edge TPU is a lightweight, embedded version of its enterprise focused Cloud Tensor Processing Unit (Cloud TPU) AI co-processor. In conjunction with a new Cloud IoT Edge stack, the chip is designed to run TensorFlow Lite ML models on Arm Linux- or Android Things based IoT gateways connected to Google Cloud services.

Nvidia launches industrial TX2i and octa-core Xavier Jetson modules

Nvidia is farther along in its attempt to bring its Pascal/CUDA-related AI technologies to embedded Linux developers. Its Jetson TX1 and TX2 computer-on-modules have found widespread adoption in embedded Linux projects for ML applications. The Jetson TX2 recently appeared in devices including Axiomtek’s eBOX560-900-FL box computer, as well as an upcoming, FPGA-equipped AIR-T Mini-ITX board for AI-enabled SDR applications.

In recent months, Nvidia has begun shipping a Jetson TX2i spin on the TX2 aimed at industrial applications. The TX2i adds -40 to 85°C support, vibration resistance, and a wider humidity range. There’s also support for ECC RAM, a 10-year operating supply lifecycle, and a 3-year warranty.

Like the Jetson TX2, the TX2i provides dual high-end Denver 2 Arm cores, a quad-core, Cortex-A57 block, and a 256-core Pascal GPU with CUDA libraries for running AI and ML algorithms. Like the TX2, the module also supplies 8GB of LPDDR4 RAM, 32GB of eMMC 5.1, and 802.11ac WiFi and Bluetooth.

Existing Jetson carrier boards work with the TX2i. Aetina just announced an ACE-N310 carrier for all the Jetson modules that matches the TX2i’s industrial temperature support and supports six simultaneous HD cameras.

The Jetson TX2 was recently joined by a more powerful new Jetson Xavier module. The Xavier core, which has already been used in Nvidia’s Drive PX Pegasus autonomous car computer board, features 8x ARMv8.2 cores and a high-end, 512-core Nvidia Volta GPU with tensor cores. It also provides 2x NVDLA deep learning engines and a 7-way VLIW vision chip. The Xavier ships with 16GB 256-bit LPDDR4 and 32GB eMMC 5.1.

Google and Nvidia are not alone in their campaigns to bring cloud AI analytics to the edge. For example, Intel’s Movidius 2 neural network accelerator chip is finding widespread adoption. Presumably, however, any future AIY Projects kits will replace the Movidius 2 with the Edge TPU.
Although Amazon has yet to reveal a neural accelerator of its own, it is perhaps still the leader in the larger race for IoT edge analytics due to the popularity of its AWS IoT stack and its AWS Greengrass software for local processing of cloud analytics software on Linux devices. Meanwhile, Microsoft is also targeting the IoT space with its Arm Linux based Azure Sphere distribution and IoT framework. Azure Sphere will initially target lower-power applications running on Cortex-A7 chips. Future versions, however, may be more robust and may include a homegrown AI component.

Join us at Open Source Summit + Embedded Linux Conference Europe in Edinburgh, UK on October 22-24, 2018, for 100+ sessions on Linux, Cloud, Containers, AI, Community, and more.

Evolutionary Algorithm Outperforms Deep-Learning Machines at Video Games

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MIT Technology Review

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August 10, 2018

With all the excitement over neural networks and deep-learning techniques, it’s easy to imagine that the world of computer science consists of little else. Neural networks, after all, have begun to outperform humans in tasks such as object and face recognition and in games such as chess, Go, and various arcade video games.

These networks are based on the way the human brain works. Nothing could have more potential than that, right?

Not quite. An entirely different type of computing has the potential to be significantly more powerful than neural networks and deep learning. This technique is based on the process that created the human brain—evolution. In other words, a sequence of iterative change and selection that produced the most complex and capable machines known to humankind—the eye, the wing, the brain, and so on. The power of evolution is a wonder to behold. …

Evolutionary computing works in an entirely different way than neural networks. The goal is to create computer code that solves a specific problem using an approach that is somewhat counterintuitive.

The conventional way to create code is to write it from first principles with a specific goal in mind.

Evolutionary computing uses a different approach. It starts with code generated entirely at random. And not just one version of it, but lots of versions, sometimes hundreds of thousands of randomly assembled pieces of code.

Hollywood Gets its Own Open Source Foundation

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TechCrunch

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August 10, 2018

Open source is everywhere now, so maybe it’s no surprise that the Academy of Motion Picture Arts and Sciences (yes, the organization behind the Oscars) today announced that it has partnered with the Linux Foundation to launch the Academy Software Foundation, a new open source foundation for developers in the motion picture and media space.

The founding members include a number of high-powered media and tech companies, including Animal Logic, Blue Sky Studios, Cisco, DreamWorks, Epic Games, Google, Intel, SideFX, Walt Disney Studios and Weta Digital.

According to a survey by the Academy, 84 percent of the industry uses open source software already, mostly for animation and visual effects. The group also found that what’s holding back open source development in the media industry is the siloed nature of the development teams across the different companies in this ecosystem.

DevSecOps: Security Automation in Enterprise DevOps

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The New Stack

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August 10, 2018

Another day, another portmanteau. DevSecOps — an expensive target on AdWords — tries to fit security into the DevOps process. It’s kind of silly because of course companies should be factoring security into their development, particularly when much of DevOps is about enterprises releasing applications faster.

Amazon Web Services’ Senior Solutions Architect Margo Cronin kicked off her talk at the European DevOps Enterprise Summit by saying how personally she doesn’t like the term DevSecOps.

The term DevSecOps “has always struck me like the last kid getting on the bus and there’s no seat available. We are treating security as an afterthought. Security has never been an afterthought with any customer I dealt with — in financial services or now at Amazon Web Services. I feel like the name doesn’t reflect the importance,” Cronin said.

In fact, with the new European regulations of GDPR, she says privacy by design and privacy by default are built right in.

“It nearly mandates you should be doing DevSecOps,” she said.

Linux Kernel Network TCP Bug Fixed

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ZDNet

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August 10, 2018

The denial of service bug had actually been patched in the Linux kernel weeks before news of it was ever announced.

Another day, another bit of security hysteria. This time around the usually reliable Carnegie Mellon University’s CERT/CC, claimed the Linux kernel’s TCP network stack could be “forced to make very expensive calls to tcp_collapse_ofo_queue() and tcp_prune_ofo_queue() for every incoming packet which can lead to a denial of service (DoS).”

True, this bug, already given the trendy name SegmentSmack, could cause DoS attacks. But it’s already been fixed.

Keynote Sneak Peek for Hyperledger Global Forum – See Who’s Speaking

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Hyperledger

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August 10, 2018

Check Out the Initial Lineup of Blockchain Leaders Speaking at Hyperledger Global Forum.

Attend Hyperledger Global Forum to see real uses of distributed ledger technologies for business and to learn how these innovative technologies run live in production networks across the globe today. Hyperledger Global Forum will cut through the hype and focus on adoption. Attendees will see first-hand how the largest organizations in the world go beyond experimentation to lead blockchain production applications with measurable impact. Make your plans now to attend the premier blockchain event of 2018.

Keynote Speakers Include:

Alexis Gauba, Co-Founder, Mechanism Labs and She(256); R&D, Blockchain at Berkeley; R&D, Thunder Token
Leanne Kemp, Founder & CEO, Everledger
Bruce Schneier, Fellow and Lecturer at the Harvard Kennedy School

Get Practical Advice for Enterprise Open Source in Free Ebook from The Linux Foundation

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The Linux Foundation

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August 9, 2018

When it comes to running and managing open source in the enterprise, experience-driven advice counts for a lot. It is very likely that your organization already runs open source, but many organizations make the mistake of reacting to the open source ecosystem instead of adopting a proactive strategy that is optimized for success. That’s where the free Enterprise Open Source ebook comes in.

This new 45-page ebook from The Linux Foundation provides a practical approach to establishing an open source strategy by outlining the actions your enterprise can take to accelerate its open source efforts. The information is based on more than two decades of professional, enterprise open source usage and development and will be most beneficial to software engineering executives, development managers, compliance experts, and senior engineers involved in enterprise open source activities.

“The availability of enterprise grade open source software is changing the way organizations develop and deliver products,” the book notes. “The combination of a transparent development community and access to public source code enables organizations to think differently about how they procure, implement, test, deploy, and maintain software. This has the potential to offer a wealth of benefits, including reduced development costs, faster product development, higher code quality standards, and more.”

Linux Deep Learning Expands: Answer Is Still 42

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ComputerWeekly

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August 9, 2018

The Linux Foundation Deep Learning Foundation (LF DLF) has announced five new members: Ciena, DiDi, Intel, Orange and Red Hat.

As an umbrella organization of The Linux Foundation itself, the LF DLF supports and sustains open source innovation in Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL).

Deep Learning is defined as an aspect of AI that is concerned with emulating the learning approach that human beings use to gain certain types of knowledge. It can be thought of as a way to automate predictive analytics and is also sometimes known as deep structured learning or hierarchical learning.

It can be supervised, semi-supervised or unsupervised and can be used to build architectures such as deep neural networks, deep belief networks and recurrent neural networks that have been used in fields including computer vision and speech recognition etc.Deep Learning concerns ‘learning data representations’ as opposed to ‘task-specific algorithms’.

WPA3: How and Why the Wi-Fi Standard Matters

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HPE Blog

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August 9, 2018

Wi-Fi Protected Access II, or WPA2, is the standard behind wireless security networking. It protects users everywhere, from coffee shops to college campuses to corporate headquarters. WPA2 may be the most widespread security standard in the world that ordinary people encounter.

With all that’s gone on since 2004, when the specification behind WPA2 was adopted, it must be considered a successful standard. But WPA2 does have some important limitations. A new version, WPA3, is a significant improvement. Products to use it are being built now, and certification for them will begin in the third quarter of 2018.

I spoke to Dan Harkins, distinguished technologist at Aruba, a Hewlett Packard Enterprise company, and author of many of the basic standards behind WPA3 to gain insights on what really matters in WPA3.

Harkins says that most of what matters in WPA3 will affect consumer deployments rather than enterprises. The improvements to consumer Wi-Fi use will be substantial and, importantly, invisible to the user.

However, key new enterprise features will appeal to the federal government and organizations that work with government agencies.

Graduation Day for Prometheus, the Open-Source Container Monitoring System

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Silicon Angle

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August 9, 2018

The Cloud Native Computing Foundation today officially graduated Prometheus from incubation, opening a new chapter in the popular open-source project’s evolution.

Prometheus is one of the most widely used systems for monitoring software container deployments. As such, the project has taken on an important role in the rise of containers, which are increasingly used to deploy applications because they’re lightweight and can easily move between different kinds of infrastructure.

Prometheus is only the second CNCF project to have graduated so far. The first was Kubernetes, the go-to framework for managing container environments. Prometheus integrates with the framework and was ranked as the most popular monitoring tool among users of the technology in a 2017 survey.