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IBM on the way back and still crazy about IOT

IBM on the way back and still crazy about IOT
by Bill McCabe on 06-16-2016 at 4:00 pm
Categories: IoT

IBM Update: IOT Transformation on Track?

There have been some interesting developments for Big Blue in the IOT space recently. Last time we reported on them, we were monitoring analysts’ worries about the semiconductor business and other divestures late last year. This year, it seems clear IBM is poised to create even more profitable opportunities in our IOT space. Let’s check in and see where they are.

Healthcare connectivity key to IOT growth

The healthcare giant, Pfizer, recently contracted with IBM to create IOT solutions for clinical trials. In a recent news article, the two have teamed up to create one of the first completely connected clinical trial environment for Pfizer’s Parkinson’s Disease medication.

For enterprise connectivity, Big Pharma has long turned to IBM for its enterprise software used in manufacturing, for finance and accounting and, of course, as an outsourced service desk delivery provider. The move to clinical uses of IBM expertise is not that much of a stretch—and cross-selling to this industry will get easier and easier as use cases — such as the Parkinson’s trial — gain traction.

In the meantime, to prepare for a 2019 launch of this experimental drug, Pfizer and IBM are setting up a “connected house” in Yorktown Heights, NY. About 200 people will live there, with IBM and Pfizer tracking them throughout their days (and, presumably) nights. This control group will help the team test the premise—and also will yield much valuable data for IBM to expand into similar uses for “connected houses.”

Stock recovering mightily- thanks to the Cloud

March saw IBM stock rebounding from lows late last year, largely due to a Morgan Stanley rating that took into account the company’s growth opportunities in the IOT. After experiencing fifteen months of declining revenue, it seems that March’s bounce-back reflects mostly IBM’s perceived power in the cloud.

“Although Amazon (AMZN) continues to lead overall in the cloud space, within the private and hybrid cloud space, IBM looks to be out front. Katy Huberty, an analyst at Morgan Stanley, believes that the market has, in fact, “underappreciated” IBM’s growth potential, as reflected by its share prices.”

The turnaround is related to IBM’s investment in “strategic imperatives… in cloud, analytics, mobile, social, and security technologies” with “IBM’s total cloud revenue (growing by) 57% on a year-over-year basis to $10.2 billion.” Analysts watching this movement will continue to upgrade the stock—and companies looking to invest in gamechanging cloud technologies to gain competitive advantage—will sit up and take notice, as well.

SAP partnership in Cloud computing allows companies to “dip a toe into the IOT”

When we talk about the IOT among ourselves, chances are we are operating from a set of assumptions that the general business community does not share. Everyone sees the opportunity. But some companies don’t have a clear path to leveraging it. Enter an IBM-SAP cloud partnership.

This partnership will allow businesses who want to “dip a toe” into IOT technologies continue to use classic, SAP enterprise infrastructure while introducing cloud-based services over time. The IOT investment might gain sign-off more quickly if the SAP-IBM partnership allows decision-makers to trust their providers more—and which companies are more ensconced in corporate IT than SAP and IBM?

“SAP’s collaboration with the 104-year-old tech giant appeals to established companies that have shied away from outsourcing operations or want use a combination of their own data centers and those in the cloud.”

First Quarter IOT Champs?

So what’s going to happen on April 18, when IBM is scheduled to report 1st Quarter earnings? That depends on who you talk to. Goldman Sachs is maintaining a neutral rating—and the stock is generally thought to be overvalued by about $3 to $10—once again, depending on who you talk to.

As we started out saying, IBM’s focus on healthcare is seen to be its “white knight” in this regard. Using its Watson capabilities, IBM is actively searching for hospital and pharmaceutical partners in oncology, in particular, to build a Watson-based information repository which will “deliver…quick access to the top-tier cancer care exclusive to MSK oncologists, enabling them to provide elite cancer treatment to their patients anywhere in the world.” Using Watson technologies to fine-tune offerings in the IOT, particularly in healthcare, seems to be IBM’s “ticket to ride” for IOT opportunities in the future.

Leveraging its global headquarters for Watson Internet of Things (IoT) in Munich, Germany will be key to IBM’s IOT momentum, as well. Their focus since the center opened in December of 2015 has been on “launching a series of new offerings, capabilities and ecosystem partners designed to extend the power of cognitive computing to the billions of connected devices, sensors and systems that comprise the IoT.” This strategy will play out to its fullest later this year and in the next five years, as the company solidifies its leadership role in the IOT space.

Stay tuned to these pages for more on the players in IOT, or give me a call with IOT recruiting needs. An IOT-enabled CIO responsible for M2M and manufacturing connectivity? Check out our latest article on the IOT-powered ride you’re in for in 2016.

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The Business of the Semiconductor Business, Part One: What Happened?

The Business of the Semiconductor Business, Part One: What Happened?
by Woz Ahmed on 06-16-2016 at 12:00 pm
Categories: EDA, IP

This is the first of an occasional series of articles on the semiconductor industry. Many column inches have covered industry consolidation and in this first article, I aim to explain how the industry reached this point. Later articles will cover subjects including China, joint ventures, emerging players like Brazil and Vietnam, monopolies, M&A, national security/national development, customer concentration, verticalisation/disintermediation, ecosystem venturing, etc. The timing of these will be erratic out of practical necessity and the order of themes…in no particular order.
Continue reading “The Business of the Semiconductor Business, Part One: What Happened?”

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The Young and the Restless, PDA vs EDA, Photonic Soaps continued…

The Young and the Restless, PDA vs EDA, Photonic Soaps continued…
by Mitch Heins on 06-16-2016 at 7:00 am
Categories: Silicon Photonics

If you’ve followed my last article, The Guiding Light and Other Photonic Soaps, you read my comments about the use of waveguides to “guide the light” in photonic integrated circuits (PICs). This article continues the soap opera theme, this time with the Young and the Restless. My point here is that I am continually struck by the dichotomies between photonic and electronic design. The more these two domains look the same, the more they are different, even down to the engineers with whom I am now finding myself working (more on that later).

The place where I’ve found the dichotomy to be most profound is in the design automation tools used for both industries. In general, the strategy so far has been to try to make photonic design automation (PDA) look as much like electronic design automation (EDA) as possible, even to making the acronyms sound alike (PDA/EDA). The idea here is that eventually these two technologies will eventually merge and since we’ve got more than 3 decades of learning on the EDA side why not right? In fact, the American Institute for Manufacturing Integrated Photonics (AIM Photonics) has already created a work group called EPDA that is looking to do just that. Upon closer inspection of the challenges though, it may turn out that there are lot of reasons for why we might want to take another approach. A few examples might be handy at this point.

The first area of dichotomy is that if ever there was a technology that cried out for “automation” it is photonics. There are several degrees of freedom and dependencies in photonics that can make for a very rich and large solution space to be explored for any given design. In EDA manual interactive processes occur at manual interactive rates, making it difficult to use these processes to explore a large design space for an optimal solution. Why then does EDA seem to want to shoe-horn photonic design into a custom layout paradigm which is inherently manual and interactive? As the saying goes, when you have a hammer, everything looks like a nail. PDA companies seem to have a different approach.

A well-known design methodology in EDA is schematic driven layout (SDL). In electronic design we start with a schematic and spend a lot of time iterating the design between the schematic and simulation before we start to do a layout. The concept of SDL works because we have logical views that share parameters with physical views in a predictable way across foundry processes. In photonics this is not necessarily the case. The functionality of photonic components is highly dependent upon their layout, physical surroundings, temperature and variations in the fabrication process and materials being used. Adding to the complexity is that the interconnect between the photonic components are not merely conductors of light but are in fact active components of the circuit. And… the coup de grace to all of this is the fact that photonic switching is usually done through evanescent coupling where physical components don’t actually touch each other and multiple wavelengths can be switched by a single component. In short we’ve broken several key assumptions for an EDA-based SDL flow and have set ourselves up for a schematic back annotation flow from hell. If you really want to twist your mind on something, think about the implications for layout versus schematic checks (LVS) in this scenario.

If I haven’t driven the point home enough yet, here is another dichotomy for our photonic soap opera. Unlike in EDA where most of the shapes are rectilinear with perpendicular angles, most photonic layout shapes are curvilinear in nature. Smooth curves, adiabatic tapers, spirals, Y-shaped splitters and joiners, grating couplers and circular resonance structures all of which can be drawn at any angle make for a very interesting exercise in a traditional EDA layout tools. Even if the tool has support for natively drawing a curved shape, it will eventually store that shape in the database as fractured discretized rectilinear polygons snapped to some design grid. This causes issues for later edits (like non-orthogonal rotations and re-connections) and physical design rule checking. See Silicon Photonics III, sections 4.2.8.1, and an excellent article in the IET Journals by members of MIT, University of Colorado and University of California, Berkley for more details on how PDA and EDA tools are trying to handle these issues.

So where does this leave us? In the end, we do need a design flow that will enable integrated electronic-photonic designs. Perhaps the “integration” or “assimilation” of PDA into EDA should not be our thrust, but instead we should be looking for convenient bridges that can be built between the two domains. There is good news in all of this and that’s where the “Young and the Restless” come into play. While most of the “not-so-young” EDA cast members were attending the Design Automation Conference last week, I was visiting photonic customers and I was amazed at the number of the “Young and the Restless” with whom I was meeting. Most of them are PhDs just out of school, highly educated and highly motivated professionals who are pushing to make integrated photonics a success. The best part about these young engineers is that they weren’t so tainted by 30+ years of ‘this is how we do EDA’. Instead they were tackling integrated photonics with a fresh new view. For a guy like me, who is still young at heart, it was refreshing to see the new enthusiasm and different thinking. It reminded me of me 30+ years ago when EDA was just beginning. Perhaps it’s time to let the PDA people do what they do best and look for ways to build bridges for them into EDA instead of trying to mold PDA into something it is not.

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IoT Tutorial: Chapter 5 – IoT Clouds and Semantic Interoperability

IoT Tutorial: Chapter 5 – IoT Clouds and Semantic Interoperability
by John Soldatos on 06-15-2016 at 12:00 pm
Categories: IoT

Semantic Interoperability of IoT Data Streams: In the previous chapter of the IoT tutorial we introduced the concept of IoT and cloud computing convergence, while presenting concrete examples of IoT/cloud infrastructures, such as popular public IoT clouds (Xively.com, Thingspeak.com). These infrastructures enable the integration of IoT data streams from different producers/providers within the very same cloud. Indeed, within an IoT cloud infrastructure, multiple IoT applications can be developed and deployed independently. Nevertheless, in most cases there is no easy way to combine data and services from diverse IoT deployments, even in cases where these deployments concern similar or even the same application domain. Consider for example two independent IoT smart energy deployments integrated in the same cloud. Even though it is very likely that their data are similar, there is no easy way to combine them in the scope of a new added-value application e.g., an application calculating the environmental performance or energy saving gains achieved based on both deployments.

This difficulty is due to the heterogeneity of the data formats of the two deployments, but mainly due to their diverse semantics as well. Indeed, data stemming from the two deployments are likely to present their data based on different semantic representations. The latter semantic representations refer to the representation of IoT resources, including units of measurement, mathematical constructs, sensor types and properties and more. This is a serious limitation of existing public IoT clouds, which are limited to supporting vertical silo applications and provide no support for more integrated horizontal applications, notably applications able to combine IoT data and services from multiple IoT deployments.

However, there are semantic web standards (such as ontologies) that provide models for the semantic unification of diverse data streams, thus providing a uniform way for representing them and reasoning over them. Such ontologies provide the means for semantic interoperability of heterogeneous IoT streams at the data level, including data streams that are integrated and stored within the same cloud infrastructure. Hence, a first step to semantic interoperability at the data level is to semantically annotate data streams prior to their streaming and integration within the cloud. This semantic annotation is prescribed by recent initiatives on IoT/cloud semantic interoperability (such as the open source OpenIoT project) and by semantic interoperability efforts within IoT standards (such as the oneM2M standard (http://onem2m.org/)).

Following the semantic annotation of the different IoT data sources/streams, their data and metadata comply with the same semantic model (e.g., ontology), which provides the means for processing the data and the metadata of the streams in a unified way and regardless of their source of origin. Processing of metadata can enable the dynamic selection and filtering of sensors, while processing of data can enable the intelligent selection and filtering of sensor data. The dynamic selection of sensors and devices can enable new model for IoT services provisioning on the cloud, such as Sensing-as-a-Service models, where end-users can dynamically define and access sensing services on demand i.e. services where sensors and sensing functions are selecting and executed dynamically.

The OpenIoT Project
The OpenIoT (openiot.eu) was one of the first projects that provided the means for semantically interoperable integration of IoT data streams in the cloud. It also demonstrated the merit of the “Sensing-as-a-Service” approach. OpenIoT is an open source project available at github, which received the Open Source Rookie award by Black Duck for 2013. OpenIoT incorporates an enhanced version of the popular Global Sensor Networks (GSN) middleware (http://lsir.epfl.ch/research/current/gsn/) (namely X-GSN), which enables the collection of data streams from different IoT sensors and devices based on popular protocols (e.g., CoAP (Constrained Application Protocol)), along with their semantic annotation according to the W3C Semantic Sensor Networks (SSN) ontology (RDF representation) and extensions over it. Semantically annotated streams are stored within a public or private cloud infrastructure (e.g., the Amazon EC2 public cloud or private clouds build with the open source OpenStack middleware). Over this cloud infrastructure, OpenIoT has implemented a range of tools for application monitoring and development.

The OpenIoT project provides the following main functionalities:

  • Deployment and Registration of a sensor or internet connected device: OpenIoT enables the integration of virtually any internet connected sensor or device to its cloud infrastructure. This is based on the interfacing of the sensor to the X-GSN middleware, which accordingly undertakes the semantic annotation of the sensor and its registration in the OpenIoT cloud. The process is facilitate by a visual tool (Schema Editor) provided by the OpenIoT project and requires the implementation of a low level interface between the device and the X-GSN middleware. The latter process is typically a matter of 1-2 man days.
  • Dynamic discovery of sensors and internet connected devices: OpenIoT provides functions and utilities for the dynamic discovery of sensors and internet connected devices, independently of their source of origin. Discovery is based on querying the RDF repository of sensors/devices, which reside in the OpenIoT cloud. The discovery process takes into account the metadata of the sensors or devices, including their type and location.
  • Visual IoT Service definition and deployment: OpenIoT offers a development environment, which enables users to develop applications (notably sensor queries) through the visual definition of data processing workflows over the semantically interoperable IoT sensors that are integrated in the cloud. The tool enables the visual construction of SPARQL queries, over the RDF representations of the sensors and their data. Accordingly, it enables the deployment of the IoT service / query in the cloud. The tools is web-based and multi-user, taking into account the sensors and services that each user is entitled to access depending on its authentication credentials.
  • IoT services visualization (via Mashups): OpenIoT provides a mashup library, which enables the visualization of the services (notably sensor queries). Mashups based visualization functionalities are provided by most public IoT cloud mentioned earlier in this tutorial. The OpenIoT mashup library and related visualization functionalities are integrated with the visual service definition and deployment functionalities within the OpenIoT integrated development environment of the project.
  • Resource Management and Optimization: OpenIoT provides several resource management and optimization functionalities (such as data caching, publish-subscribe optimization) and more.

OpenIoT has already a community of users, which take advantage of the project for research and academic purposes, even though the project has already been deployed in the scope of pilot deployments in enterprise environments. Following figures provide snapshots of the OpenIoT architecture, the OpenIoT mashups and the tool for visual definition of IoT services.



Applications of Data Level Semantic Interoperability
Data-level semantic interoperability is only a small part of the wider problem of IoT interoperability, which has been introduced in an earlier chapter. However, the semantic interoperability across IoT streams from different sources provides already a sound basis for a number of added-value applications in various areas including:

  • Smart Cities: In smart cities, there is nowadays a need to integrate and manage information stemming from a large number of different IoT deployments, which have been planned and carried out independently from each other. In several cases these deployments concerns similar applications (e.g., smart energy, urban mobility) and provide similar data (e.g., data about transport or energy). Nowadays there is no easy way to combine data from these deployments in order to implement new integrated management applications (e.g., city-wide monitoring of environmental performance) or even operational applications (e.g., holistic management of urban mobility). Data level semantic interoperability (based on platforms such as OpenIoT) can indeed facilitate the development of such added-value management infrastructures and application. This is discussed in more detailed in the scope of subsequent chapter on urban mobility.
  • IoT Experimentation: The lack of data-level semantic interoperability is a set back to the development of IoT experiments based on data from multiple IoT testbeds (e.g., air quality data from different infrastructures). The adoption of approaches such as OpenIoT enable the design and execution of more integrated experiments that leverage data from diverse IoT sources, systems and platforms.

These are some examples of the merits of semantic interoperability of diverse IoT resources, which is only scratching the surface of the wider problem of IoT interoperability. Additional aspects and solutions for IoT interoperability will be discussed in subsequent chapters, as part of IoT interoperability standards and applications that leverage interoperability functionalities.

Resources for Further Reading
1) OpenIoT is an open source project. Its source code and documentation are available through the following links:

2)An overview description of OpenIoT is available at the following paper:
John Soldatos, Nikos Kefalakis, Manfred Hauswirth, Martin Serrano, Jean-Paul Calbimonte, Mehdi Riahi, Karl Aberer, Prem Prakash Jayaraman, Arkady B. Zaslavsky, Ivana Podnar Zarko, Lea Skorin-Kapov, Reinhard Herzog: OpenIoT: Open Source Internet-of-Things in the Cloud. OpenIoT@SoftCOM 2014: 13-25

3) Some the resource management capabilities of OpenIoT are discussed at:
Kefalakis, S. Petris, C. Georgoulis, J. Soldatos, “Open Source Semantic Web Infrastructure for Managing IoT Resources in the Cloud”, Chapter in the book: Internet of Things: Principles and Paradigms, Rajkumar Buyya and Amir Vahid Dastjerdi (eds.).

View all IoT Tutorial Chapters

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IoT Tutorial: Chapter 4 – Internet of Things in the Clouds

IoT Tutorial: Chapter 4 – Internet of Things in the Clouds
by John Soldatos on 06-15-2016 at 7:00 am
Categories: IoT

The advent of cloud computing has acted as a catalyst for the development and deployment of scalable Internet-of-Things business models and applications. Therefore, IoT and cloud are nowadays two very closely affiliated future internet technologies, which go hand-in-hand in non-trivial IoT deployments. Furthermore, most modern IoT ecosystems up-to-date are cloud-based, as will be illustrated in subsequent chapters of the tutorial. Prior to describing the essence of IoT and cloud computing integration, we briefly introduce the main cloud computing concepts. Αn in-depth presentation of cloud computing can be found in relevant textbooks such as Mastering Cloud Computing by Rajkumar Buyya et. Al.

Cloud Computing Basics
Cloud computing is the next evolutionary step in Internet-based computing, which provides the means for delivering ICT resources as a service. The ICT resources that can be delivered through cloud computing model include computing power, computing infrastructure (e.g., servers and/or storage resources), applications, business processes and more. Cloud computing infrastructures and services have the following characteristics, which typically differentiate them from similar (distributed computing) technologies:

  • Elasticity and the ability to scale up and down: Cloud computing services can scale upwards during high periods of demand and downward during periods of lighter demand. This elastic nature of cloud computing facilitates the implementation of flexibly scalable business models, e.g., through enabling enterprises to use more or less resources as their business grows or shrinks.
  • Self-service provisioning and automatic deprovisioning: Contrary to conventional web-based Application Service Providers (ASP) models (e.g., web hosting), cloud computing enables easy access to cloud services without a lengthy provisioning process. In cloud computing, both provisioning and de-provisioning of resources can take place automatically.
  • Application programming interfaces (APIs): Cloud services are accessible via APIs, which enable applications and data sources to communicate with each other.
  • Billing and metering of service usage in a pay-as-you-go model: Cloud services are associated with a utility-based pay-as-you-go model. To this end, they provide the means for metering resource usage and subsequently issuing bills.
  • Performance monitoring and measuring: Cloud computing infrastructures provide a service management environment along with an integrated approach for managing physical environments and IT systems.
  • Security: Cloud computing infrastructures offer security functionalities towards safeguarding critical data and fulfilling customers’ compliance requirements.

The two main business drivers behind the adoption of a cloud computing model and associated services including:

  • Business Agility:Cloud computing alleviates tedious IT procurement processes, since it facilitates flexible, timely and on-demand access to computing resources (i.e. compute cycles, storage) as needed to meet business targets.
  • Reduced Capital Expenses: Cloud computing holds the promise to lead to reduced capital expenses (i.e. IT capital investments) (CAPEX), through enabling conversion of CAPEX to operational expenses (i.e. paying per month, per user for each service) (OPEX). This is due to the fact that cloud computing enables flexible planning and elastic provisioning of resources instead of upfront overprovisioning.

Depending on the types of resources that are accessed as a service, cloud computing is associated with different service delivery models.

  • Infrastructure as a Service (IaaS): IaaS deals with the delivery of storage and computing resources towards supporting custom business solutions. Enterprises opt for an IaaS cloud computing model in order to benefit from lower prices, the ability to aggregate resources, accelerated deployment, as well as increased and customized security. The most prominent example of IaaS service Amazon’s Elastic Compute Cloud (EC2), which uses the Xen open-source hypervisor to create and manage virtual machines.
  • Platform as a Service (PaaS): PaaS provides development environments for creating cloud-ready business applications. It provides a deeper set of capabilities comparing to IaaS, including development, middleware, and deployment capabilities. PaaS services create and encourage deep ecosystem of partners who commit to this environment. Typical examples of PaaS services are Google’s App Engine and Microsoft’s Azure cloud environment, which both provide a workflow engine, development tools, a testing environment, database integration functionalities, as well as third-party tools and services.
  • Software as a Service (SaaS): SaaS services enable access to purpose-built business applications in the cloud. Such services provide the pay-go-go, reduced CAPEX and elastic properties of cloud computing infrastructures.

Cloud services can be offered through infrastructures (clouds) that are publicly accessible (i.e. public cloud services), but also by privately owned infrastructures (i.e. private cloud services). Furthermore, it is possible to offer services supporting by both public and private clouds, which are characterized as hybrid cloud services.

IoT / Cloud Convergence
Internet-of-Things can benefit from the scalability, performance and pay-as-you-go nature of cloud computing infrastructures. Indeed, as IoT applications produce large volumes of data and comprise multiple computational components (e.g., data processing and analytics algorithms), their integration with cloud computing infrastructures could provide them with opportunities for cost-effective on-demand scaling. As prominent examples consider the following settings:

  • A Small Medium Enterprise (SME) developing an energy management IoT product, targeting smart homes and smart buildings. By streaming the data of the product (e.g., sensors and WSN data) into the cloud it can accommodate its growth needs in a scalable and cost effective fashion. As the SMEs acquires more customers and performs more deployments of its product, it is able to collect and manage growing volumes of data in a scalable way, thus taking advantage of a “pay-as-you-grow” model. Moreover, cloud integration allows the SME to store and process massive datasets collected from multiple (rather than a single) deployments.
  • A smart city can benefit from the cloud-based deployment of its IoT systems and applications. A city is likely to deploy many IoT applications, such as applications for smart energy management, smart water management, smart transport management, urban mobility of the citizens and more. These applications comprise multiple sensors and devices, along with computational components. Furthermore, they are likely to produce very large data volumes. Cloud integration enables the city to host these data and applications in a cost-effective way. Furthermore, the elasticity of the cloud can directly support expansions to these applications, but also the rapid deployment of new ones without major concerns about the provisioning of the required cloud computing resources.
  • A cloud computing provider offering pubic cloud services can extend them to the IoT area, through enabling third-parties to access its infrastructure in order to integrate IoT data and/or computational components operating over IoT devices. The provider can offer IoT data access and services in a pay-as-you-fashion, through enabling third-parties to access resources of its infrastructure and accordingly to charge them in a utility-based fashion.

These motivating examples illustrate the merit and need for converging IoT and cloud computing infrastructure. Despite these merits, this convergence has always been challenging mainly due to the conflicting properties of IoT and cloud infrastructures, in particular, IoT devices tend to be location specific, resource constrained, expensive (in terms of development/ deployment cost) and generally inflexible (in terms of resource access and availability).

On the other hand, cloud computing resources are typically location independent and inexpensive, while at the same time providing rapid and flexibly elasticity. In order to alleviate these incompatibilities, sensors and devices are virtualized prior to integrating their data and services in the cloud, in order to enable their distribution across any cloud resources. Furthermore, service and sensor discovery functionalities are implementing on the cloud in order to enable the discovery of services and sensors that reside in different locations.

Based on these principles the IoT/cloud convergence efforts have started since over a decade i.e. since they very early days of IoT and cloud computing. Early efforts in the research community (i.e. during 2005-2009) have focused on streaming sensor and WSN data in a cloud infrastructure. Since 2007 we have also witnessed the emergence of public IoT clouds, including commercial efforts. One of the earliest efforts has been the famous Pachube.com infrastructure (used extensively for radiation detection and production of radiation maps during earthquakes in Japan). Pachube.com has evolved (following several evolutions and acquisitions of this infrastructure) to Xively.com, which is nowadays one of the most prominent public IoT clouds.

Nevertheless, there are tens of other public IoT clouds as well, such as ThingsWorx, ThingsSpeak, Sensor-Cloud, Realtime.io and more. The list is certainly non-exhaustive. These public IoT clouds offer commercial pay-as-you-go access to end-users wishing to deploying IoT applications on the cloud. Most of them come with developer friendly tools, which enable the development of cloud applications, thus acting like a PaaS for IoT in the cloud.

Similarly to cloud computing infrastructures, IoT/cloud infrastructures and related services can be classified to the following models:

  • Infrastructure-as-a-Service (IaaS) IoT/Clouds: These services provide the means for accessing sensors and actuator in the cloud. The associated business model involves the IoT/Cloud provide to act either as data or sensor provider. IaaS services for IoT provide access control to resources as a prerequisite for the offering of related pay-as-you-go services.
  • Platform-as-a-Service (PaaS) IoT/Clouds: This is the most widespread model for IoT/cloud services, given that it is the model provided by all public IoT/cloud infrastructures outlined above. As already illustrate most public IoT clouds come with a range of tools and related environments for applications development and deployment in a cloud environment. A main characteristic of PaaS IoT services is that they provide access to data, not to hardware. This is a clear differentiator comparing to IaaS.
  • Software-as-a-Service (SaaS) IoT/Clouds: SaaS IoT services are the ones enabling their uses to access complete IoT-based software applications through the cloud, on-demand and in a pay-as-you-go fashion. As soon as sensors and IoT devices are not visible, SaaS IoT applications resemble very much conventional cloud-based SaaS applications. There are however cases where the IoT dimension is strong and evident, such as applications involving selection of sensors and combination of data from the selected sensors in an integrated applications. Several of these applications are commonly called Sensing-as-a-Service, given that they provide on-demand access to the services of multiple sensors. Note that SaaS IoT applications are typically built over a PaaS infrastructure and enable utility based business models involving IoT software and services.

These definitions and examples provide an overview of IoT and cloud convergence and why it is important and useful. More and more IoT applications are nowadays integrated with the cloud in order to benefit from its performance, business agility and pay-as-you-go characteristics.

In following chapters of the tutorial, we will present how to maximize the benefits of the cloud for IoT, through ensuring semantic interoperability of IoT data and services in the cloud, thus enabling advanced data analytics applications, but also integration of a wide range of vertical (silo) IoT applications that are nowadays available in areas such as smart energy, smart transport and smart cities. We will also illustrate the benefits of IoT/cloud integration for specific areas and segments of IoT, such as IoT-based wearable computing.

View all IoT Tutorial Chapters

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Is the Intel Cash Cow in Danger?

Is the Intel Cash Cow in Danger?
by Daniel Nenni on 06-14-2016 at 4:00 pm
Categories: Arm, Events, Foundries, Intel Foundry

There was an interesting panel at the Silicon Summit sponsored by the Global Semiconductor Alliance (GSA) on “Designing for the Cloud.” It was led by Linley Gwennap (The Linley Group) with Ivo Bolsons (Xilinx), Ian Ferguson (ARM), and Steve Pawloski (Micron). Missing of course was Intel which derives close to 30% of its revenue from their Data Center Group (DCG), also known as their cash cow. There were however quite a few Intel people in attendance who were willing to talk, off the record of course.

Unfortunately the presentation slides are behind a firewall on the GSA site but I have them in front of me so I will give you a quick summary. But first, let’s talk about the Intel DCG and the changing cloud landscape.

DCG is recognized as one of the primary Intel growth engines along with IoT (which is a joke that I will cover another time). Unfortunately growth is decelerating from 18% in 2014 to 11% in 2015 and in the first quarter of 2016 DCG revenue growth is down to 8%. DCG revenue growth should still hit double digits this year but after that it is a little bit cloudy.

ARM based servers are definitely a threat. Ian Ferguson admitted that it has been 8 years since ARM entered the server business with little success (1% market share). The same could be said about ARM entering the mobile business many years ago with little success but they of course prevailed (99% market share). ARM has publicly stated that they are still aiming for 25% market share by 2020 and Ian shared the ARM Server Core Beliefs:

[LIST=1]

  • We will compromise on single threaded performance for non-linear gains in power efficiency (architecture licensees may make different trade-offs).
  • Right balance between standardization and innovation (Enable an ecosystem to coalesce with enough “greenfield” areas to empower opportunities for differentiation).
  • It’s about the system stupid (Once the CPU is “good enough” most important areas are memory, I/O, and on-chip hardware accelerators).

    According to Ian, boxes are being deployed worldwide with China leading the effort. Why China you ask? Because China, the most populous country in the world, consumes almost half of the semiconductors made and will have the single largest cloud demand in the world, absolutely. And China wants control over the silicon for security reasons to “avoid the perils of reliance on American technology.” This alone will maim the Intel cash cow.

    Qualcomm is the largest ARM licensee to announce a China JV (joint venture with Guizhou Huaxintong Semi-Conductor Technology) which will start by selling Qualcomm’s own server designs this year. This will be followed by new versions designed by the JV specifically for the China cloud and finally a completely new chip aimed directly at Intel.

    In addition to the ARM threat there is also a joint development agreement between China and the IBM OpenPOWER foundation for server chips. IBM was at the Design Automation Conference last week openly recruiting people in Austin for the China JV.

    AMD also announced a joint venture with China which includes x86 processor technology for server chip development. Again, China wants to make their own chips, which is also why China is allowing TSMC, GlobalFoundries, and others to build fabs in China.

    Given all that, plus Intel losing the process lead at 10nm and 7nm to TSMC and Samsung, continued double digit growth for Intel’s primary growth engine (DCG) is very hard to believe, just my opinion of course.

    • Posted on

    Is the U.S. ready to adopt a new financial model to support microelectronics?

    Is the U.S. ready to adopt a new financial model to support microelectronics?
    by Tom Dillinger on 06-14-2016 at 12:00 pm
    Categories: Foundries

    Amidst all the active news about new process introductions at 16/14/10/7nm and the status of next-generation lithography development, there was a recent press release that could have as large an impact upon the microelectronics industry in the United States. A groundbreaking ceremony was recently held in Marcy, New York for a new fab, to be run by ams AG, formerly known as austriamicrosystems AG (link here).

    ams AG focuses on the design and manufacture of analog IC’s, with specific emphasis on sensors, power management, and wireless networking applications.

    This fab will initially focus on products at the 130nm process node, with plans for more advanced nodes in the future. The goal is to be operational by YE’2017, with high volume manufacturing commencing in early 2018.

    There are two aspects to this announcement that are particularly noteworthy, IMHO.

    First, there remain significant capacity constraints industry-wide at older process nodes.

    Second, the financial arrangement provided to support this new fab is extremely unique, at least in the United States. The land and 360,000 sq. ft. building complex located at the SUNY Polytechnic Institute will continue to be owned by SUNY, with the facility available to ams AG on a 20-year lease “at very attractive rates”.

    ams AG will invest ~$2B to ramp the new facility, with a commensurate number of new jobs, estimated at ~700. (Indirect jobs outside ams AG to support this facility will increase the total additional employment.)

    To be sure, there have been business incubator facilities established as extensions to public institutions, such as universities. The goal of these relatively small sites is to facilitate the spin-off of new entrepreneurial pursuits, usually based on advanced academic research. These university parks offer low overhead, attractive financial terms for these small firms.

    Yet, to my knowledge, this is the first example in the U.S. of a public institution establishing a build/lease financial model for an established microelectronics company to equip and maintain a new fab for high volume manufacturing.

    When the deal was first announced last year, the SUNY Poly site development team said, “At the direction of the Governor, SUNY Poly has entered into a strategic research, development, and manufacturing partnership with ams AG.”

    Governor Cuomo himself said, “This is a transformative moment, that will make a difference in peoples’ lives in the Mohawk Valley for generations to come.”

    New York, and specifically, Governor Cuomo, have been spearheading public-private partnerships in the microelectronics industry, as led by the Nanotech Initiative at SUNY-Albany. This latest deal takes the financial relationship for a production manufacturing fab to a new level.

    Is this new partnership model for microelectronics indeed something that public resources in the U.S. will increasingly support? Is the U.S. at a disadvantage compared to other countries due to its reticence to invest in these kinds of partnerships? Will other states and/or the federal government be willing to make similar economic investments to grow semiconductor manufacturing here?

    How do public taxpayers minimize the risks of financial loss in these ventures? (Although not in the microelectronics area, the economic stimulus provided by the federal government to Solyndra in 2009 comes to mind.)

    Time will tell, I suppose, whether the New York-ams AG partnership is indeed a public-private “win-win”. It is definitely a new, innovative approach toward revitalization of a region and of a domestic manufacturing industry.

    PS. It was reported that Governor Cuomo was at one time considering a Democratic Presidential campaign in 2016 – that would no doubt have resulted in some interesting economic development-related discussions among the candidates. 🙂

    -chipguy

    Posted on

    Climbing the Infinite Verification Mountain

    Climbing the Infinite Verification Mountain
    by Bernard Murphy on 06-14-2016 at 7:00 am
    Categories: Cadence, EDA

    Many years ago I read a great little book by Rudy Rucker called “Infinity and the Mind”. This book attempts to explain the many classes of mathematical infinity (cardinals) to non-specialists. As he gets to the more abstract levels of infinity, the author has to resort to an analogy to give a feel for extendible and other cardinal classes.

    He asks you to imagine climbing a mountain called Mount On (you’ll have to read the book to understand the name). You climb at infinite speed and, after a day’s climbing, you look up and see more mountain stretching ahead, looking much the same as the stretch you just climbed. Anyone who’s ever climbed a mountain or a hill knows exactly how this feels. The author’s point is that no matter how fast you climb, there’s always more mountain ahead – it seems like you never get close to the top.

    The reason I bring this up is that verification feels very similar, largely because the problem space continues to explode faster than we can contain. Cadence hosted a thought-provoking lunch at DAC this year (Seamlessly Connected Verification Engines? What Does It Take?) which I felt was very relevant to this topic. Jim Hogan opened and underlined the challenge. The amount of knowledge/data we have to deal with is increasing supra-exponentially. Today it’s doubling every 13 months. By 2020 when the IoT is in full swing, it is expected to double every 12 hours. The capabilities we will need to handle that volume with high quality are going to demand a (currently) almost inconceivable level of verification, touching almost everything in the stack. We’re going to have to seriously up our quality game, as Jim put it.

    On that daunting note, the panel started with where the tools play best today. Alex Starr (Fellow at AMD) felt that formal was primarily useful for IP and subsystem, emulation is good for system but (software-based) simulation is struggling everywhere. That said, simulation still has an advantage over emulation in being able to verify mixed-signal designs. But a better and longer-term AMS solution may be to model virtual prototyping together with analog models in order to get system-level coverage with mixed-signal. AMD has invested over several years to make this work and it’s now paying off.

    Narendra Konda (Head of Verification at NVIDIA) further emphasized the need for tools in the verification flow to play together, especially with virtual prototyping (VP). He pointed to the need for VP to play well with emulation and FPGA prototyping (FP), also for assertion-based verification (ABV) to play well in these flows. They are simulating 1B gates with a rapidly growing software stack across large banks of test-cases. They have to put a lot of this in VP to get practical run-times.

    The perennial question of team interoperability came up of course. You can make the tools completely interoperable but that doesn’t help as much as it could if design and verification teams stick to their silos of expertise. Alex agreed this could be a problem – a good example is in power management verification where you have to span all the way from software applications potentially to physical design. His view was that this takes education and of course improvement in the tools, like portability of test cases.

    Narendra took a harder line; designers don’t get to have comfort zones – they adapt or die. Fusing tools together is the bigger problem, but it is being solved by stages. For NVIDIA it took 1½ years working with Cadence to get VP, emulation and FP working well together. He views this collaboration as a necessary price for staying ahead. They had the same problem 2-3 years ago with ABV and the same approach to solving the problem – now this is also starting to work. Dealing with automotive safety and reliability requirements is a new frontier; there is no productive tool today in this area. Narendra expects this will take another 8-10 months to get to a solution.

    The panel wrapped up on current deficiencies in cross-platform methodologies. All emphasized more need for standards, especially for interoperability between platforms from different vendors. Some of that is happening with the Portable Stimulus standard, but more still needs to be done, for example in normalizing coverage metrics between vendors.

    In verification, performance is never where it needs to be. Narendra saw a need for something in-between the speed of emulation and prototyping (with emulation setup times and debug-ability of course). He felt doubling the current speed of emulation would help. Alex agreed and also said that for serious software regression, emulation and FP aren’t fast enough. There needs to be more of an emphasis on hybrid modeling between hardware platforms and VP, where it’s more feasible to get within range of real-time performance. This echoed Narendra’s earlier point about the need for VP hybrid modeling.

    I found continued references to VP here and in other meetings particularly interesting. Software-driven verification with bigger software stacks and more test-cases really do drive a need to model more of the system in VP-like platforms, dropping into FP and emulation where needed. This need can only grow. Perhaps VP is becoming the new emulation and emulation is becoming the new simulation.

    The vendors are doing a great job advancing what they do, and they’re clearly partnering effectively with customers to build those solutions, but the top of the verification mountain keeps receding into the clouds (there’s a pun in there somewhere) and probably always will. Meantime you can read more about NVIDIAs success with Cadence emulation HERE.

    More articles by Bernard…