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IBM Demonstrates Blockchain Progress and Clients

IBM Demonstrates Blockchain Progress and Clients
by Alan Radding on 12-29-2016 at 4:00 pm

IBM must have laid off its lawyers or something since never before has the company seemed so ready to reveal clients by name and the projects they’re engaged in. That has been going on for months and recently it has accelerated. Credit IBM’s eagerness to get blockchain established fast and show progress with the open community HyperLedger Project.

Exploring the use of blockchain to bring safer food


Since early in 2016 IBM announced almost 20 companies and projects involving blockchain. A bunch are financial services as you would expect. A couple of government entities are included. And then, there is Walmart, a household name if ever there was one. Walmart is turning to blockchain to manage its supply chain, particularly in regard to food safety and food provenance (tracking where the food came from and its path from source to shelf to the customer).


Here’s how it works: With blockchain, food products can be digitally tracked from an ecosystem of suppliers to store shelves and ultimately to consumers. When applied to the food supply chain, digital product information such as farm origination details, batch numbers, factory and processing data, expiration dates, storage temperatures and shipping detail are digitally connected to food items and the information is entered into the blockchain along every step of the process. Each piece of information provides critical data points that could potentially reveal food safety issues with the product. The information captured and if there is a problem it becomes easy to track down where the process went wrong.


Furthermore, the record created by the blockchain can also help retailers better manage the shelf-life of products in individual stores, and further strengthen safeguards related to food authenticity. In short, Walmart gains better visibility into the supply chain, logistics and food safety as they create a new model for food traceability, supply chain transparency, and auditability using IBM Blockchain based on the open source Linux Foundation Hyperledger Project fabric.


Walmart adds: “As advocates of promoting greater transparency in the food system for our customers, we look forward to working with IBM and Tsinghua University to explore how this technology might be used as a more effective food traceability solution,” said Frank Yiannas, Vice President, Food Safety, Walmart. If successful, it might get rolled out to North America and the rest of the world.


IBM is not expecting blockchain to emerge full blown overnight. As it noted in its announcement. blockchain has the potential to transform the way industries conduct business transactions. This will require a complete ecosystem of industry players working together, allowing businesses to benefit from the network effect of blockchain. To that end IBM introduced a blockchain ecosystem to help accelerate the creation of blockchain networks.


And Walmart isn’t the only early adopter of the HyperLedger and blockchain. The financial services industry is a primary target. For example, the Bank of Tokyo-Mitsubishi UFJ (BTMU) and IBM agreed to examine the design, management and execution of contracts among business partners using blockchain. This is one of the first projects built on the Hyperledger Project fabric, an open-source blockchain platform, to use blockchain for real-life contract management on the IBM Cloud. IBM and BTMU have built a prototype of smart contracts on a blockchain to improve the efficiency and accountability of service level agreements in multi-party business interactions.


Another financial services player, the CLS Group (CLS), a provider of risk management and operational services for the global foreign exchange (FX) market, announced its intent to release a payment netting service, CLS Netting will use blockchain for buy-side and sell-side institutions’ FX trades that are settled outside the CLS settlement service. The system will have a Hyperledger-based platform, which delivers a standardized suite of post-trade and risk mitigation services for the entire FX market.


To make blockchain easy and secure, IBM has set up a LinuxONE z System as a cloud service for organizations requiring a secure environment for blockchain networks. IBM is targeting this service to organizations in regulated industries. The service will allow companies to test and run blockchain projects that handle private data. The secure blockchain cloud environment is designed for organizations that need to prove blockchain is safe for themselves and for their trading partners, whether customers or other parties.


As blockchain gains traction and organizations begin to evaluate cloud-based production environments for their first blockchain projects, they are exploring ways to maximize the security and compliance of the technology for business-critical applications. Security is critical not just within the blockchain itself but with all the technology touching the blockchain ledger.


With advanced features that help protect data and ensure the integrity of the overall network, LinuxONE is designed to meet the stringent security requirements of the financial, health care, and government sectors while helping foster compliance. As blockchain ramps up it potentially can drive massive numbers of transactions to the z. Maybe even triggering another discount as with mobile transactions.


DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Please follow DancingDinosaur on Twitter, @mainframeblog. See more of his IT writing attechnologywriter.com and here.


NetSpeed Bridges the Gap Between Architecture and Implementation

NetSpeed Bridges the Gap Between Architecture and Implementation
by Mitch Heins on 12-29-2016 at 11:30 am

This is part II of an article covering NetSpeed’s network-on-chip (NoC) offerings. This article dives a little deeper into what a NoC is and how NetSpeed’s network synthesis tool, NocStudio, helps system architects optimize a NoC for their system-on-a-chip (SoC) design.

Traditionally IC designers have used proprietary buses, crossbars and switch fabrics to connect their on-chip IPs. These proprietary architectures are fine for simpler ICs but as SoCs become larger and more heterogeneous in nature and foreign IPs are brought in from various sources it has become increasingly difficult to integrate the design using these fabrics. Additionally, dedicated interconnection between multiple IPs requires more wiring, creating congestion and inflating die sizes while possibly leading to increased power consumption to drive the longer interconnects.

The alternative is to use a network-on-chip (NoC) that makes use of shared interconnect resources (links and routers) as opposed to dedicated wiring between IPs to reduce the overall wiring required of the inter-IP connections by as much as 30% to 50%. At the simplest level, the NoC is a grid of point-to-point links between the various IP. At the intersections of the grid are specialized on-chip routers that steer data to their destinations. Just as in off-chip networks, data moves from its origin to its destination through a process known as store and forward (SaF) where data is broken into pieces known as packets. Packets contain the data being transferred, called the payload, along with a data header that specifies origin, destination, and a unique ID to establish packet ordering for final re-assembly at the destination. The size of the payload and associated buffers at each network node is determined by the design of the network.

As packets arrive at a router, they are “stored” in a buffer and a hardware arbiter in the router determines the next downstream location for the packet. The arbiter configures a shared switch and then “forwards” the packet from the buffer to the next node through the switch. Once the packet has moved to the next node the router releases the switch resources so that subsequent packets can use them. Individual packets make their way to their destination in the most efficient way as prescribed by competing traffic on the network. This is repeated until all of the packets reach their destination where they are reassembled in the correct order based on their ordering ID.

This is admittedly a highly simplified view but you get the gist. There are loads of PhD dissertations on how best to arbitrate channels given different workloads, avoid deadlocking conditions, and make trade-offs for different network configurations depending on the types of data being sent and latency and quality of services (QoS) desired. In short, this is a daunting task for even the most advanced system level designers and can make or break a SoC. The greater the number and variety of cores and modules on the SoC, the more complex the NoC. Data coherency and data security add additional hardware levels on top of this basic physical network level that must also be comprehended.

NetSpeed offers multiple value propositions to aid in the process of designing a NoC. These include but are not limited to a seasoned team of professionals that understand network architectures and a set of configurable ready-to-go NoC IP for handling end-to-end QoS requirements within a heterogeneous environment with a mixture of both coherent and non-coherent agents. That in itself is noteworthy, but what got my attention is that they aren’t just supplying IP. NetSpeed has managed to bridge a difficult gap between architecture and implementation.

NetSpeed’s NocStudio design environment gives the system designer the all-important capability to do “what if” analysis and trade-offs of the various different NoC architectures. It enables the system designer to work at the application level (coherency, QoS, deadlock avoidance), the transport level (different protocol support), the network level (traffic-based optimization, including power analysis), the link level (support for sub-networks, clusters and virtual channels) and the physical level.

Designers capture IP components and connectivity, define performance requirements and establish high level network requirements between IPs such as bandwidth, latency sensitivity and required QoS. What is different is that where typical system tools stop, NetSpeed keeps going. They took on the challenging task of generating the implementation RTL for all of the logic (routers, arbiters, buffers, coherency controllers, virtual channel logic, pipelining etc.) including taking into account the floorplan, power and performance requirements of the SoC.

This is not an easy task. There are always trade-offs that must be made to ensure the design is implementable given die size and timing/power budgets. Giving the system designer the ability to iterate based on implementation details is important because it’s at the architectural stage where there is the most leverage to accommodate changes imposed by realities of the implementation.

NocStudio allows designers to drop all of the desired IP blocks into a floorplan and the tool can then optimize the placement of the IPs and alter the network configuration to meet the various designer specified system requirements. Alternatively, the tool can be given a floorplan and asked to synthesize the best possible network configuration given the floorplan it is given.

The real trick, however, is being able to automatically generate a correct-by-construction synthesizable RTL implementation of the NoC. Teaching an engineer to write synthesizable RTL code is one thing. Teaching that same engineer what his RTL is supposed to be doing to implement the carefully designed NoC is a whole different and more difficult story. Verifying he implemented what you asked of him is yet another difficult task. NocStudio eliminates the need for this by generating correct-by-construction RTL that implements all of the trade-offs made by the system designer.

And if that weren’t enough the tool also generates a verification test bench and C++ functional models that can be used in the design flow to ensure closure on the final implementation.

NetSpeed enables not only the design of an incredibly robust NoC, but also the implementation and verification of the same. In my book that’s a pretty complete solution.

See Also:
NetSpeed Leverages Machine Learning for Automotive IC End-to-End QoS Solutions
Automating Front-End SoC Design with NetSpeed’s On-Chip Network IP
More data at netspeedsystems.com


They Kill Pedestrians, Don’t They?

They Kill Pedestrians, Don’t They?
by Roger C. Lanctot on 12-28-2016 at 4:00 pm

I came upon the scene of a crash investigation yesterday afternoon in my hometown of Herndon, Va. A mother and two children were hit by a 20-year-old motorist making a right turn at an intersection. I did not see the crash, but I strongly suspect the motorist was looking left to anticipate oncoming traffic and never noticed the pedestrians preparing to step off the curb to her right.


It was strangely reassuring to see the magnitude of official response in the form of nearly 10 police vehicles, not including three motorcycle riding officers, along with a circling helicopter (most likely from a local broadcaster) and on-the-ground camera crews recording the investigation. The mother and her children, though injured, were expected to survive the incident. The police reported that neither speed nor alcohol were thought to be involved.

I briefly joined onlookers crowding the intersection to see what was going on. The event highlighted the fact that pedestrian fatalities spiked in 2015 – rising 10% to 5,376 from 4,910 in 2014. A third of all highway fatalities in the U.S. occur at intersections, according to data from the National Highway Traffic Safety Administration and pedestrians account for 15% of all fatalities.

Regulators, researchers and observers had been clucking over the steady decline totaling 28% in pedestrian fatalities from 1975 to 2009, but 2015’s total is 31% higher than the lowest point of pedestrian fatalities in 2009. In October, the Fairfax County Virginia police published an analysis of pedestrian crashes noting that the Herndon/Reston area was one of the safest in the county.


Fairfax County pedestrian crash data analysis – Jan. 1, 2011 and July 28, 2016

In the words of one published report: “The Traffic Division Crime Analyst identified 11 areas where there has been a higher incidence of pedestrian fatal or serious injury crashes over that period, and none of them are anywhere near the Reston or Herndon areas.”

Those findings are cold comfort for one mother and her children this Christmas. The intense police response and the attention of passers-by suggested that pedestrian crashes and fatalities are something of a novelty and worthy of close scrutiny. The reality is that the novelty is wearing off and pedestrian fatalities are on the rise… and it is an unexplained rise.

Cars and their drivers need better situational awareness. As hostile as some car enthusiasts are to self-driving cars, at least self-driving cars are equipped with camera, radar and in some instances LiDAR systems that are capable of detecting objects in blind spots – including pedestrians and bicyclists.

Right turn blindness to pedestrians on the right side of the car – on or off the curb – is a common enough occurrence worthy of a safety system mandate be it a camera or short-range radar or both. Analysis of traffic and crash data seems to lull us all into a false sense of security, not just residents of the Herndon/Reston area of Fairfax County, Va. We have not solved the rising toll of highway fatalities and pedestrians are especially vulnerable and available in volume during the holidays. Drive carefully.


Solving a Murder Case with IoT Devices

Solving a Murder Case with IoT Devices
by Daniel Payne on 12-28-2016 at 12:00 pm

I watch a lot of Netflix and there are so many detective movies and series for me to enjoy where I try and match wits with the bad guys and figure out who is guilty a few seconds before the law enforcement characters do. On TV and with our movies there is often critical evidence extracted from desktop computers, laptops, hard drives, smart phones and even automobile GPS devices. With an increase in connected devices used each day we are seeing more technology applied to help solve crimes, so it was no surprise to read the recent headline story about an Amazon Echo device confiscated by the police to possible help them solve a murder case in Bentonville, Arkansas.

The Amazon Echo device has the following characteristics:

  • WiFi connected
  • Tower speaker
  • 7 Microphone array
  • Voice activated by saying “Alexa, Amazon or Echo”
  • Remote control
  • Answers your questions
  • Music playback
  • Can create to-do lists
  • Set an alarm
  • Stream a podcast
  • Play an audio book
  • Provide weather reports, traffic and other real time info
  • Act as a home automation hub

Early adopters of the Echo were Amazon Prime members who could buy the device in early 2015, while the masses had to wait until June 2015 to buy one and start using it.

Semiconductor content inside of the Amazon Echo was provided by iFixIt in a teardown report:

  • Step-down regulator IC from TI
  • Ultra low-power stereo audio codec from TI
  • 15W filter-free Class D stereo amplifier from TI
  • Digital Media Processor, TI
  • LPDDR1 RAM, Samsung
  • 4GB iNAND Ultra Flash Memory, SanDisk
  • WiFi and Bluetooth module, Qualcomm
  • Power management IC, TI
  • Programmable 9-output LED Driver, TI
  • Low-power stereo ADC, TI
  • TTL Logic, TI

Our suspect in this particular murder case is named James and the victim was Victor who was strangled and drowned in James hot tub back in November 2015. The police have looked at the phone and text message records of suspect James. Some of the other IoT devices in the home include:

  • Nest thermostat
  • Honeywell alarm system

Because the Echo device is constantly listening to what’s going on inside your home, the police were naturally curious to find out if this particular Echo had overheard the conversations, fighting or actual murder. Since the Echo device listens with its 7 microphones and sends the info into the cloud, controlled by Amazon, the police have asked Amazon to hand over the audio from the time of the murder. Amazon officials have denied the initial request based on court records, and won’t release customer data from Echo, “without a valid and binding legal demand properly served on us. Amazon objects to over broad or otherwise inappropriate demands as a matter of course.”

This stand off sounds like deja vu from the Apple case where they wouldn’t unlock a drug dealer’s iPhone back in March 2016. At least in the case of the iPhone there appears to be an industry out there that knows how to unlock without Apple’s permission. Not so for the Echo devices.

Even though the local police have confiscated the Amazon Echo from the suspect, none of the audio is actually stored on the device, rather it would only be processed and stored on the cloud that Amazon controls. Nobody really knows how much data Amazon is gathering from their Echo devices, so perhaps this particular case will reveal what Amazon really does with always on, IoT devices like Echo that hear and decipher our speech. I really hope that justice is served in this murder case, with or without help from Amazon. We can only expect that law enforcement will continue to request IoT and consumer electronics companies to help them solve crimes using the amazing, new abilities of always-on devices that can hear and view what we are doing throughout our daily lives.


3 Tips for Securing Home Cameras

3 Tips for Securing Home Cameras
by Matthew Rosenquist on 12-28-2016 at 7:00 am

Installing a home surveillance camera system can add great benefits but also may introduce new risks to privacy and network security. The goal is to increase the security and peace of mind, while avoiding cybersecurity threats. Here are three tips to consider when purchasing, installing, and configuring your new home camera system.

The Risks

Home internet connected cameras are targets for cybercriminals. Recently a number of large Internet-of-Things (IoT) attacks have occurred where hacker have compromise hundreds of thousands of devices and enlisted them in massive botnets. These collections of ordinary devices, such as IP Cameras, DVRs, and home routers, are then directed by their bot-herder to all send network traffic to a target destination. The massive flow of data overwhelms the target site and makes it unavailable. A recent attack against DYN, an Internet DNS lookup service, took out much of the U.S. East Coast access to Twitter, Spotify, Netflix, Amazon, Tumblr, Reddit, PayPal and other sites. Hacking home internet connected devices has become a powerful tool for cybercriminals. That home camera you are considering could add to the problem and even be used by hackers to spy on you!

3 Tips for Securing Home Cameras

Most attacks are not incredibly sophisticated. They can be traced back to insecurely designed products, absence of patches, and poor installation configurations. Security does not need to be difficult or time consuming, but it does require forethought and care.

Here are my Top 3 recommendations for Securing Home Cameras:

1. Choose the vendor wisely.
It all starts with choice. If privacy and security is important to you, it should be part of your purchase criteria. Not all home camera vendors are equal. Look for ones which works hard to keep safe your privacy and security. How do you tell? Go out to their webpage and look beyond their marketing advertisements, as everyone will splash the word “secure” everywhere. The question you must consider is do they take it seriously and deliver? Look to see if they publish security updates, is there a security team, and do they talk in detail how they secure their products and services.

No product is safe for indefinitely, especially in the Internet of Things (IoT) world. What is important, is the level of commitment a company places on keeping their products secure for their customers. It is highly desirable if they are producing security patches and explaining what vulnerabilities they are closing. Transparency is a sign of trust. For your part, you must be sure to patch and keep products up to date.

Many companies don’t bother to have a security team. It is a major warning sign if the vendor is without such expertise. It means they are not likely designing in robust security features, don’t have people looking at vulnerabilities, not developing patches, and not verifying security in updates.

Those with a security team should be open in the controls designed into the product, testing criteria, certifications, and what bugs they have closed. Professionals work hard and want to build trust with their customers. I like companies who also have bug bounty programs that reward white-hat hackers who find vulnerabilities and bring them to the attention of the company. Having the hacker community helping make your products more secure is a good thing.

The first and most important step is yours. You must select a trustworthy partner who is supplying the camera, software, and any additional services. Look at reviews, comments from owners, and by security professionals who test these cameras. Choose wisely and you will be rewarded.

2. Setup in a non-sensitive area
Cameras are great ways to watch over your home. But expect at some time, even the best products, could be compromised for a period of time. Therefore, placement is hugely important. Entry, common areas, and even watching over babies are great places to setup cameras. Bedrooms, changing rooms, bathrooms, and other private areas are not optimal. Many modern cameras have microphones and other sensors. So even in common areas, you might want to consider what you are saying. Home cameras are tailored for easy setup and minimal fuss when dealing with data. Most work with cloud services which store data and make it accessible to you anywhere on most devices. A great feature, but that also means the recordings are not directly under your control and it is another place for hackers to target. So consider what data you want in the cloud. You don’t want embarrassing or private clips appearing on the internet. Where you setup the camera will determine the limits of how uncomfortable such situations become.

3. Change default passwords

Home cameras come with a number of default settings to facilitate easy setup. Most don’t need to be modified, but the default password should be changed! Change them to a unique and strong password which you don’t use anywhere else. Store it somewhere safe. Worst case, if you forget it, the can typically be reset on the camera itself. Many of the current variants of IoT botnets are targeting the vast number of devices which still have default passwords, which are published on the Internet, thus granting them full access to cameras. Some vendors are now forcing users to change the password upon installation, but many still don’t. Don’t be an easy target. Be smart and change the default password, as it makes a significant difference.

Home cameras a great. They provide a new sense of security and flexibility to our modern lives. It is important to balance those benefits with the accompanying risks. By following a few steps, you are increasing the controls and making yourself a less attractive target. Enjoy your new camera with the confidence of security and privacy.

Interested in more? Follow me on Twitter (@Matt_Rosenquist), Steemit, and LinkedIn to hear insights and what is going on in cybersecurity.


The 2017 Leading Edge Semiconductor Landscape

The 2017 Leading Edge Semiconductor Landscape
by Scotten Jones on 12-27-2016 at 6:00 pm

In early September of 2016 I published an article “The 2016 Leading Edge Semiconductor Landscape” that proved to be very popular with many views, comments and reposting’s. Since I wrote that article a lot of new data has become available enabling some projections to be replaced by actual values and new analysis and projections to be made.
Continue reading “The 2017 Leading Edge Semiconductor Landscape”


IoT and a few of my favourite things

IoT and a few of my favourite things
by John Moor on 12-27-2016 at 5:00 pm

I was at the 27th Hewlett Packard Colloquium on Information Security at Royal Holloway, University of London this week and met Alan Stockey of RiskingIT.com. Alan told me about a little ditty he wrote on IoT security to the tune of “My Favourite Things” from the Sound of Music. Amused me and he’s allowed me to share it at this festive time of year – hope you enjoy and thanks Alan (and RHUL – I learned a great deal about law enforcement hacking, block chain and computing on encrypted data).

Here goes…

Ding-dong Internet of Things
Smart new devices with wi-re-less hidden
Intelligent kettles with 5 billion minions!
Big data packages, next wondrous thing?
They capture the pulse of those Internet things

White coloured tablets, and Fitbits seduce us
Cameras and light bulbs, but doorbells? confuse us
Heart-trackers record when we’ve died, then send strings
So tempting to hack all these Internet things

When the BOT bites
When the Phish stings
Make us feel so mad
If we simply secure all our Internet things
Then we won’t feel so bad

Bluetooth in fridges need regular patches
TV and sex-toy voyeurs send dispatches
Hand-dryers in washrooms blow hot and then sing
Just a few more of those Internet things

Driverless cars could escape many crashes, with
Cyber patrolmen bug-testing their caches?
Intelligent toilets that flush and then ping
It’s to twenty-first century gadgets we cling

When the BOT bites
When the Phish stings
Make us feel so mad
When we’ve turned off all those Internet things
Would we all feel so glad

We really don’t need that bad… repeat to fade…

(c) Stockey2016

Oh, one last thing – don’t forget to use the IoTSF best practice guidelines and security framework if you want to avoid being a headline (they are free):

https://iotsecurityfoundation.org/best-practice-guidelines/


Intel Spreadtrum ARM SoCs

Intel Spreadtrum ARM SoCs
by Daniel Nenni on 12-27-2016 at 12:00 pm

In June of 2013 Edward Snowden copied and leaked classified information from the National Security Agency (NSA). His actions exposed numerous surveillance programs that many governments around the world reacted to, including China. In September of 2013 China Vice Premier Ma Kai declared semiconductors a key sector for the security of China. As a result Chinese mobile carriers replaced American made networking equipment (Cisco) and the Chinese government has pledged more than $100B to internal semiconductor development including SoCs. A modern SoC has billions of gates and it only takes a handful to create a back door, right?

To secure smartphones the Chinese government tapped Spreadtrum Communications CEO Leo Li to make special-order “safe” SoCs to foil foreign spies. At the end of 2013 the publicly held Spreadtrum was acquired by Tsinghua Unigroup (backed by the Chinese government) for $1.78B. As a result, Spreadtrum has grown rapidly and now has R&D facilities in Shanghai, Beijing Tianjin, Suzhou, Hangzhou, Chengdu, Xiamen, San Diego, San Jose, Finland, and India. Spreadtrum is privately held now so revenues are not reported but from what I understand they will come very close to $2B in 2016 making them one of the top ten fabless semiconductor companies.

About Spreadtrum Communications
As an affiliate of Tsinghua Unigroup, Ltd, Spreadtrum Communications is a fabless semiconductor company that develops mobile chipset platforms for smartphones, feature phones and other consumer electronics products, supporting 2G, 3G and 4G wireless communications standards. Spreadtrum’s solutions combine its highly integrated, power-efficient chipsets with customizable software and reference designs in a complete turnkey platform, enabling customers to achieve faster design cycles with a lower development cost. Spreadtrum’s customers include global and China-based manufacturers developing mobile products for consumers in China and emerging markets around the world. For more information, visit www.spreadtrum.com.

The majority of Spreadtrum SoCs are ARM based using off-the-shelf ARM cores. That changed of course with the 2015 $1.5B Intel investment:

“China is now the largest consumption market for smartphones and has the largest number of Internet users in the world,” said Brian Krzanich, Intel CEO. “These agreements with Tsinghua Unigroup underscore Intel’s 29-year-long history of investing in and working in China. This partnership will also enhance our ability to support a wider range of mobile customers in China and the rest of the world by more quickly delivering a broader portfolio of Intel architecture and communications technology solutions.”

Spreadtrum is now sampling Intel based 14nm SoCs as well as TSMC/ARM based 16nm SOCs. This is standard Chinese strategy of developing multiple chip types and letting the best design win. The question is: Can off-the-shelf ARM and Intel cores compete against the custom ARM architecture SoCs designed by Qualcomm? The answer is no, not on the mid-to-high end smartphones. We can see this in the QCOM vs MediaTek SoC benchmarks and resulting China market shares (MKT uses off-the-shelf ARM cores).

It has always been my opinion that in order to compete with QCOM in the SoC business you will have to license the ARM architecture and create your own cores and that is what Spreadtrum has now done. Notice that Spreadtrum has an R&D center in San Jose (down the street from ARM), that is the group that is doing the custom ARM SoC architecture and from what I have heard they already taped out their first version (ARM Cortex-A53 Class) on TSMC 28nm with FinFET versions to follow.

The billion dollar questions is: Will the Spreadtrum custom SOCs benchmark better than Qualcomm and MediaTek? The answer of course is that they do not have to for rapid adoption in the Chinese market. Remember, Spreadtrum is backed by the Chinese government for the security of China, absolutely.


The Other Half of AI

The Other Half of AI
by Bernard Murphy on 12-27-2016 at 7:00 am

I touched earlier on challenges that can appear in AI systems which operate as black-boxes, particularly in deep learning systems. Problems are limited when applied to simple recognition tasks, e.g. recognizing a speed limit posted on a sign. In these cases, the recognition task is (from a human viewpoint) simply choosing from among a limited set of easily distinguished options, so an expert observer can easily determine if/when the system made a bad decision.

But as AI is extended to more complex tasks, it becomes increasingly difficult to accurately grade the performance of those systems. Certainly, there will still be many cases where an expert observer can classify performance easily enough. But what about cases where the expert observer isn’t sure? Where is the student surpassing the master and where is the student simply wrong?

This reminded me of an important Indian mathematician, Srinivasa Ramanujan, whose methods were in some ways as opaque as current AI systems. There was a movie release this year – The Man Who Knew Infinity – covering Ramanujan’s career and challenges. He had an incredible natural genius for mathematics, but chose to present results with little or no evidence for how he got there (apparently because he couldn’t afford the extra paper required to write out the proofs).

This lack of demonstrated proofs raised concerns among professional mathematicians of Ramanujan’s time. Mathematical rigor requires a displayed proof leading to the result, so that other experts can validate (or disprove) the claim. This is not unlike the above-mentioned concern with modern deep learning systems. For conclusions which a human expert can easily classify there is no problem, but for more complex assertions a bald statement of a conclusion is insufficient. We want to know how the system arrived at that conclusion for one of two reasons: it might be wrong and if so we want to know where it went wrong so we can fix it (perhaps by improving the training set), or it might be right in which case we’d like to know why so we can improve our own understanding.

Recent work at UC Berkeley and the Max Planck Institute for Informatics has made progress in this direction for deep learning systems. The underlying mechanics are the same but they use multiple training datasets, to deliver a conclusion and to justify sub-steps leading to that conclusion. The domain for the study is image recognition, specifically determining aspects of what is happening in an image (for example, what sport is being played).

The research team noted that a system-generated chain of reasoning may not correspond to how a human expert would think of a problem, so a better approach needs some user friendliness. Instead of presenting the user with a proof, let them ask questions which the system should answer, an approach known as visual question answering (VQA). While this may not lead to mathematically rigorous proofs, it seems very appropriate for many domains where a human expert wants to feel sufficiently convinced but may not need every possible proof point.

The method requires two principle components: VQA augmented by spatial attention where the system looks at localized image features (such as a figure) to draw conclusions (this person is holding a bat), and more global activity recognition/explanation (this is a baseball game). These datasets were annotated through crowdsourcing with “proposition because explanation” labels.

The research team wanted also to point to an object supporting a proposition, for example if the VQA asserted “this person is holding a bat”, they wanted to point to the bat. This is where the attention aspect of the model becomes important. You could imagine this kind of capability being critical in a medical diagnosis where perhaps a key aspect of the diagnosis rests on an assumption that a dark spot in an X-ray corresponds to a tumor. “This patient has a tumor as shown in this X-ray” is hardly a sufficient proposition, whereas “this patient has a tumor in the liver as shown at this location in this X-ray” is much more usable information and something a doctor could confirm or challenge.

As we aim to push AI into more complex domains, this kind of justification process will become increasingly important. Which of us would trust a medical diagnosis delivered by a machine without a medical expert first reviewing and approving that diagnosis? Collision avoidance in a car may not allow time to review before taking action, but subsequent litigation may quite possibly demand review on whether the action taken was reasonable. Even (and perhaps especially) where AI is being used to guide scientific discovery or proof, the AI will need to demonstrate a chain of reasoning which human experts can test for robustness. It will be a very long time before “because my AI system said so” will be considered a sufficient alternative to peer review. Which is really the point. Important decisions, whether made by people or machines, should not be exempt from peer review.

You can read the UCB/MPI arXiv paper HERE.

More articles by Bernard…


Real Time Virtualization, How Hard Can it Be?

Real Time Virtualization, How Hard Can it Be?
by Daniel Payne on 12-26-2016 at 12:00 pm

My first exposure to running something virtual on a computer was when I decided to run the Windows OS on my MacBook Pro using software provided by Parallels. With that virtualization I was able to run the Quicken app under Windows on my MacBook Pro, along with the popular Internet Explorer web browser. The app performance on virtualized Windows isn’t the highest, so I don’t watch YouTube videos or stream Netflix on the Windows side.
Continue reading “Real Time Virtualization, How Hard Can it Be?”