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Blog Review – Jan. 20 2014

Monday, January 20th, 2014

By Caroline Hayes, Senior Editor/EDA

Cloud services are gathering for IBM as a blog shares its plans. Cadence looks ahead to March’s DVCon, there is also a recipe for microcontroller selection, advice on multicore SoCs, CDC verification and power optimized designs and Seattle’s automotive engineers go into over-drive.

IBM has big plans for cloud computing and Erich Clementi, relays news of a $1.2billion investment to increase IBM’s cloud cover by the end of 2014 and the role of recent acquisition, SoftLayer, in the infrastructure.

Sound advice from Jacob Beningo, ARM, who cautions against choosing a microcontroller before hardware and software engineers thrash out the system design, ready for a “rational decision”.

Looking ahead to DVCon 2014, Richard Goering, Cadence, looks to March’s Design and Verification Conference in San Jose. This year, the focus on design increases with two new Technical Program Committee vice chairs, David Black of Doulos and Martin Barnasconi, NXP. Goering also helps with budgets, pointing out that the cost of registering for the event increases after January 28.

Reflecting on the popularity of heterogeneous multicore SoCs at International CES 2014 (the Vybrid platform from Freescale and the Jacinto 6 platform from Texas Instruments, among them) Kamran Shah looks at two Mentor Graphics’ videos demonstrating how communication between ARM© Cortex©-M4 and Cortex-A15 cores can ease design scenarios.

Another event, coming soon (Jan 23) is the Seattle Chapter of the SAE (Society of Automotive Engineers). John Byler will address attendees on how automotive electronic designers and testers handle hardware/software integration at the chip, board and network levels. Then buckle up for a tour of the SAE Formula 1 manufacturing area.

In praise of formal analysis for CDC verification of fast-to-slow clocks, Roger Hughes, Real-Intent, continues the discussion with part two of an informative, detailed blog series.

Unashamedly proclaiming the virtues of Calypto’s PowerPro automated power reduction tool, Rob Eccles, looks at verifying power-optimized designs with SLEC and LEC.

Sensors and Algorithms Challenge IoT Developers

Tuesday, December 10th, 2013

By John Blyler, Content Officer

Challenges abound as designers deal with the analog nature of sensors, IP issues and the new algorithms required by the IoT.

Sensors represent both the great enabler and unique challenge for the evolving Internet of Things (IoT). Innovation in the market will come from surprising places. These are just a few of the observations shared by ARM’s Willard Tu, Director of Embedded and Diya Soubra, CPU Product Manager. “System Engineered Design” caught up with them during the recent ARM Tech Con. What follows is a portion of that conversation. – JB

Blyler: Everyone talks about the importance of sensors to enable the Internet of Things (IoT) but few seem to appreciate what that means.  Would you elaborate?

Tu: Sensors are one of our key initiatives, especially from a microcontroller viewpoint (more on that shortly). But there is another aspect to sensors that both designers and even companies overlook, namely, the algorithms for processing the sensor data. These algorithms, from companies like Hillcrest, bring a unique value to the IoT market. And the algorithm software represents a real intellectual property (IP). I think people are missing out on the IP that is being created there.

Blyler: So you think that most people overlook the IP aspects and simply focus on the processing challenges needed to condition analog sensor signals into a digital output?

Tu: Processing power is critical, which is where distributed local and cloud computing comes in. But there are many other factors, such as energy harvesting to power sensors in areas you never thought of before. Both body and mess network communication challenges are another factor. Conversely, one enabler of sensors is their inexpensive cost. Ten years ago, an accelerometer was a really expensive piece of silicon for an automotive airbag system. Now, they are everywhere, even in cell phones which are very cost sensitive.

Blyler: Is this volume cost decrease due to innovation in MEMS design and manufacturing?

Tu: Yes, the MEMS market has evolved immensely (see Figure 1) and that’s the reason. And I think there is still a lot of evolution there. You see a lot of new comers with MEMS applications but I think you’ll see a lot of consolidation because only the strong will survive.

Figure 1: MEMS market trends as presented by Jeremie Bouchaud of the HIS at the MEMS Congress, 2013.

Soubra: Another factor is that few vendors use only one sensor, but rather a lot of sensors. A common example is multi-sensor accelerometers (see Figure 2): one sensor gives you a good pitch, the others give you yaw and roll.  So you will always need three of four sensors, which means that you have to have software to handle all of them.

Figure 2: Device Orientation – 6 Degrees of Freedom. (Courtesy of Hillcrest)

Blyler: Do you mean software to control and integrate data from the various sensors or software algorithms to deal with the resulting data?

Tu: Both – Software is needed to control and ensure the accuracy of the sensors. But developers are also doing more contextual awareness and predictive analysis. By contextual, I mean that a smart phone turns on when it’s being held next to my head. Predictive refers to what I’ll do next, i.e., having the software anticipate my next actions. Algorithms enable those capabilities

This is the next evolution in handling the data. You can use sensor fusion (sensors plus processors) to create contextual awareness.  That’s what people are doing today.  But how does that evolve into predictive algorithms? Anticipating what you want is even more complex than contextual awareness. It’s like using Apple’s Siri to anticipate when you are hungry and then order for you. Another example is monitoring a person’s glucose level to determine if they are hungry – because their glucose levels have dropped. It could be very intuitive or predictive down the road.

Blyler: These smart algorithms are another reason why processing power is a key enabler in the IoT evolution.

Tu: What you really need is scalable processing power. Sensors require a microcontroller, something with analog inputs. But there are still lots of designers who ask, “Why do you need to integrate the microcontroller with the sensor? It’s just an accelerometer.” They seem to forget that data acquisition is an analog process. The sensor data that is acquired must be conditioned and digitized to be useful in contextual or predictive applications. And that requires lots processing.

Another thing designers forget about is calibration (see Figure 3). Calibration is a big deal to get the accuracy necessary for all the contextual awareness applications. Calibration of the MEMS device is only part of the issue. The device must be recalibrated as part of the larger system once it is soldered and packaged to a board, to deal with temperature affects (of the solder) and flexing of the board. All of these things play a part of the system-level calibration.

Figure3 : Proper interpretation and calibration of the sensor data is critical. The performance of the core fusion algorithm depends on the quality of the input data. (Courtesy of Hillcrest)

You might think that, well, the sensors guys should do that. But the sensor guys are good at making a MEMS device. Some MEMS manufactures are vertically integrating to handle calibration issues, but others just want to make the device. This is another area where innovate IP can grow, i.e., around the calibration of the MEMS device to the system.

Blyler: Where will innovation come from as the IoT evolves?

Tu: I think the ecosystem is where innovation will emerge. Part of this will come from taking application developed in one area and applying them to another. Recently, I talked to several automotive developers. They admitted that they lack of expertise in developing certain types of algorithms – the same algorithms that companies like Hillcrest have already created for mobile consumer applications. I would like to introduce the automotive market to an algorithm company (like Hillcrest), a sensor platform provider (like Movea) ant a few other leaders in the mobile space.

I think you will see IP creation in that space. That is where innovation is coming, by taking that raw sensor data and making it do something useful.

Blyler: Consolidation is occurring throughout the world of semiconductor design and manufacturing, especially at the lower process nodes. Do you see similar consolidation happening in the sensor space.

Tu: Right now there is an explosion of sensor companies, but there will be a consolidation down the road. The question one should ask is if integration key to the sensor and IoT space. I don’t know.  As a company, ARM would like to see a microcontroller (MCU) next to every sensor or sensor cluster – whether it is directly integrated to the sensor array or not. This is where scalability is important. Processing will need to be distributed; low power processing near the sensor with higher performance processing in the cloud.  It is very difficult to put a high-powered fan based system in a sensor. It just won’t happen. You have to be very low power near the sensor.

Not only is the sensor node a very power constrained environment but it is also resource constrained, e.g., memory. That’s why embedded memory is critical – be it OTP or flash. In addition to low power, it is the cost of that memory is actually more influencing than the CPU.

Blyler: Thank you.

What Powers the IoT?

Wednesday, October 16th, 2013

By Stephan Ohr, Gartner

Powering the Internet of Things (IoT) is a special challenge, says Gartner analyst Stephan Ohr, especially for the wireless sensor nodes (WSNs) that must collect and report data on their environmental states (temperature, pressure, humidity, vibration and the like). While the majority of WSNs will harness nearby power sources and batteries, there will be as many as 10% of the sensor nodes that must be entirely self-powering. Often located in places where it is difficult or impossible to replace batteries, these remote sensor nodes must continue to function for 20 years or more.

Two research and development efforts focus on self-powering remote sensor nodes: One effort looks at energy harvesting devices, which gather power from ambient sources. The major types of energy harvesting devices include specialized solar cells, vibration and motion energy harvesters, and devices that take advantage of thermal gradients warm and cool surfaces. Research and development concentrated on reducing the size and cost of these devices, and making their energy gathering more efficient. But even in their current state of development, these devices could add up to a half-billion in revenues per year within the next five years.

The other R&D effort concentrates on low-power analog semiconductors which will elevate the milli-volt outputs of energy harvesting devices to the levels necessary for powering sensors, microcontrollers, and wireless transceivers. These devices include DC-DC boost converters, sensor signal conditioning amplifiers, and, in some cases, data converter ICs which transform the analog sensor signals to digital patterns the microcontroller can utilize. Broadline analog suppliers like Linear Technology Corp. and Analog Devices have added low-power ICs to their product portfolios. In addition to boosting low-level signals, they use very little power themselves. LTC’s low-power parts, for example, have a quiescent current rating of 1.3 micro-amps. Other companies liked Advanced Linear Devices (ALD) have been working on low-threshold electronics for years, and Texas Instruments has a lineup of specialized power management devices for WSN applications.

Ohr’s projections on energy harvesting will be part of his talk on “Powering the Internet of Things” at the Sainte Claire Hotel, San Jose, CA on October 24, 2013. (Admission is free, but advance registration is required The Internet of Things – A Disruption and an Evolution)

Source: Gartner Research (Oct 2013)

Stephan (“Steve”) Ohr is the Research Director for Analog ICs, Sensors and Power Management devices at Gartner, Inc., and focuses on markets that promise semiconductor revenue growth. His recent reports have explored custom power management ICs for smart phones and tablets, the impact of Apple’s choices on the MEMs sensor industry, and a competitive landscape for MEMs sensor suppliers.

Ohr’s engineering degree, a BS in Industrial Engineering, comes from the New Jersey Institute of Technology (the Newark College of Engineering) and his graduate degree, an MA in sociology, comes from Rutgers.

Mixed Signal and Microcontrollers Enable IoT

Wednesday, October 16th, 2013

By John Blyler

The Internet of Things (IoT) has become such a hot topic that many business and technical experts see it as a key enabler for the fourth industrial revolution – following the steam engine, the conveyor belt and the first phase of IT automation technology (McKinsey Report). Still, for all the hype, the IoT concept seems hard to define.

From a technical standpoint, the IoT refers to the information system that uses smart sensors and embedded systems that connect wired or wirelessly via Internet protocols. ARM defines IoT as, “a collection of smart, sensor-enabled physical objects, and the networks, servers and services that interact with them. It is a trend and not a single sector or market.” How do these interpretations relate to the real world?

“There are two ways in which the “things” in the IoT interact with the physical world around us,” explains Diya Soubra, CPU Product Manager for ARM’s Processor Division. “First they convert physical (analog) data into information and second they act in the physical world based on information. An example of the first way is a temperature sensor that reports temperature while an example of the second way is a door lock opens upon receiving a text message.”

For many in the chip design and embedded space, IoT seems like the latest iteration of the computer-communication convergence heralded from the last decade. But this time, a new element has been added to the mix, namely, sensor systems. This addition means that the role of analog and mixed signal system must now extend beyond RF and wireless devices to include smart sensors. This combination of analog mixed signal, RF-wireless and digital microcontrollers has increase the complexity and confusion among chip, board, package and end product semiconductor developers.

“Microcontrollers (MCUs) targeting IoT applications are becoming analog-intensive due to sensors, AD converters, RF, Power Management and other analog interfaces and modules that they integrate in addition to digital processor and memory,” says Mladen Nizic, Engineering Director for Mixed Signal Solutions at Cadence Design Systems. “Therefore, challenges and methodology are determined not by the processor, but by what is being integrated around it. This makes it difficult for digital designers to integrate such large amounts of analog. Often, analog or mixed-signal skills need to be in charge of SoC integration, or the digital and analog designer must work very closely to realize the system in silicon.”

The connected devices that make up the IoT must be able to communicate via the Internet. This means the addition of wired or wireless analog functionality to the sensors and devices. But a microcontroller is needed to convert the analog signal to digital and to run the Internet Protocol software stacks. This is why IoT requires a mix of digital (Internet) and analog (physical world) integration.

Team Players?

Just how difficult is it for designers – especially digital – to incorporate analog and mix signal functionality into their SoCs? Soubra puts it this way (see Figure 1): “In the market, these are two distinct disciplines. Analogue is much harder to design and has its set of custom tools. Digital is easier since it is simpler to design, and it has its own tools. In the past (prior to the emergence of IoT devices), Team A designed the digital part of the system while Team B designed the analog part separately. Then, these two distinct subsystems where combined and tested to see which one failed. Upon failure, both teams adjusted their designs and the process was repeated until the system worked as a whole. These different groups using different tools resulted in a lengthy, time consuming process.”

Contrast that approach with the current design cycle where the entire mixed signal designers (Teams A and B) work together from the start as one project using one tool and one team. All tool vendors have offerings to do this today. New tools allow viewing the digital and analog parts at various levels and allow mixed simulations. Every year, the tools become more sophisticated to handle ever more complex designs.

Figure 1: Concurrent, OA-based mixed-signal implementations. (Courtesy of Cadence)

Simulation and IP

Today, all of the major chip- and board-level EDA and IP tool vendors have modeling and simulation tools that support mixed signal designs directly (see Figure 2).

Figure 2: Block diagram of pressures-temperature control and simulation system. (Courtesy Cadence)

Verification of the growing analog mixed-signal portion of SoCs is leading to better behavioral models, which abstract the analog upward to the register transfer level (RTL). This improvement provides a more consistent handoff between the analog and digital boundaries. Another improvement is the use of real number models (RNMs), which enable the discrete time transformations needed for pure digital solver simulation of analog mixed-signal verification. This approach enables faster simulation speeds for event-driven real-time models – a benefit over behavioral models like Verilog-A.

AMS simulations are also using assertion techniques to improve verification – especially in interface testing. Another important trend is the use of statistical analysis to handle both the analog nature of mixed signals and the increasing number of operational modes. (See, “Moore’s Cycle, Fifth Horseman, Mixed Signals, and IP Stress”).

Figure: Cadence’s Mladen Nizic (background right) talk about mixed-signal technology with John Blyler. (Photo courtesy of Lani Wong)

For digital designers, there is a lot to learn in the integration of analog systems. However, the availability of ready-to-use analog IP does make it much easier than in the past. That’s one reason why the analog IP market has grown considerable in the last several years and will continue that trend. As reported earlier this year, the wireless chip market will be the leading growth segment for the semiconductor industry in 2013, predicts IHS iSuppli Semiconductor (“Semiconductor Growth Turns Wireless”).

The report states that original-equipment-manufacturer (OEM) spending on semiconductors for wireless applications will rise by 13.5% this year to reach a value of $69.6 billion – up from $62.3 billion in 2012.

The design and development of wireless and cellular chips – part of the IoT connectivity equation – reflects a continuing need for related semiconductor IP. All wireless devices and cell phones rely on RF and analog mixed-signal (AMS) integrated circuits to convert radio signals into digital data, which can be passed to a baseband processor for data processing. That’s why a “wireless” search on the website reveals list after list of IP companies providing MIPI controllers, ADCs, DACs, PHY and MAC cores, LNAs, PAs, mixers, PLLs, VCOs, audio/video codecs, Viterbi encoders/decoders, and more.

Real-World Examples

“Many traditional analog parts are adding more intelligence to the design and some of them use microcontrollers to do so,” observes Joseph Yiu, Embedded Technology Specialist at ARM. “One example is an SoC from Analog Device (ADuCM360) that contains a 24-bit data acquisition system with multichannel analog-to-digital converters (ADCs), an 32-bit ARM Cortex-M3 processor, and Flash/EE memory. Direct interfacing is provided to external sensors in both wired and battery-powered applications.”

But, as Soubra mentioned earlier, the second way in which the IoT interacts with the physical world is to act on information – in other words, through the use of digital-to-analog converters (DACs). An example of a chip that converts digital signals back to the physical analog world is SmartBond DA14580. This System-on-Chip (SoC) is used to connect keyboards, mice and remote controls wirelessly to tablets, laptops and smart TVs. It consists of Bluetooth subsystem, a 32 -bit ARM Cortex M0 microcontroller, antenna connection and GPIO interfaces.

Challenges Ahead

In addition to tools that simulated both analog, mixed signal and digital designs, perhaps the next most critical challenge in IoT hardware and software development is the lack of standards.

“The industry needs to converge on the standard(s) on communications for IoT applications to enable information flow among different type of devices,” stressed Wang, software will be the key to the flourish of IoT applications, as demonstrated by ARM’s recent acquisition of Sensinode.” A Finnish software company, Sensinode builds a variation of the Internet Protocols (IP) designed for IoT device connection. Specifically, the company develops to the 6LoWPAN standard, a compression format for IPv6 that is designed for low-power, low-bandwidth wireless links.

If IoT devices are to receive widespread adoption by consumers, then security of the data collected and acted upon by these devices must be robust. (Security will be covered in future articles).

Analog and digital integration, interface and communication standards, and system-level security have always been challenges faced by leading edge designers. The only thing that changes is the increasing complexity of the designs. With the dawning of the IoT, that complexity will spread from every physical world sensor node to every cloud-based server receiving data from or transmitting to that node. Perhaps this complexity spreading will ultimately be the biggest challenge faced by today’s intrepid designers.

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