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Verification of algorithm-intensive systems is a long, costly process. Studies show that the majority of flaws in embedded systems are introduced at the specification stage, but are not detected until late in the development process. These flaws are the dominant cause of project delays and a major contributor to engineering costs. For algorithm-intensive systems —including systems with communications, audio, video, imaging, and navigation functions— these delays and costs are exploding as system complexity increases. It doesn't have to be this way. Many designers of algorithm-intensive systems already have the tools they need to get verification under control. Engineers can use these same tools to build system models that help them find and correct problems earlier in the development process. This can not only reduce verification time, but also improves the performance of their designs. In this article, we'll explain three practical approaches to early verification that make this possible. First, let's examine why the current algorithm verification process is inefficient and error-prone. In a typical workflow, designs start with algorithm developers, who pass the design to hardware and software teams using specification documents.

A product can be of low quality for several reasons, such as it was shoddily manufactured, its components were improperly designed, its architecture was poorly conceived, and the product's requirements were poorly understood. Quality must be designed in. You can't test out enough bugs to deliver a high-quality product. The quality assurance (QA) process is vital for the delivery of a satisfactory system. In this last part in this series, we will concentrate on portions of the methodology particularly aimed at improving the quality of the resulting system. The software testing techniques described earlier in this series constitute one component of quality assurance, but the pursuit of quality extends throughout the design flow. For example, settling on the proper requirements and specification cannot be overlooked as an important determinant of quality. If the system is too difficult to design, it will probably be difficult to keep it working properly.

Power and energy are key design considerations across a spectrum of computing solutions, from supercomputers and data centers to handheld phones and other mobile computers. A large body of work focuses on managing power and improving energy efficiency. While prior work is easily summarized in two words—"Avoid waste!"—the challenge is figuring out where and why waste happens and determining how to avoid it. In this article, I discuss how, at a general level, many inefficiencies, or waste, stem from the inherent way system architects address the complex trade-offs in the system-design process. I discuss common design practices that lead to power inefficiencies in typical systems and provide an intuitive categorization of high-level approaches to addressing them. The goal is to provide practitioners—whether in systems, packaging, algorithms, user interfaces, or databases—a set of tools, or "recipes," to systematically reason about and optimize power in their respective domains.

Xilinx Inc. today introduced the architecture for a new Extensible Processing Platform they claim will deliver unrivaled levels of system performance, flexibility and integration to developers of a wide variety of embedded systems. The ARM Cortex-A9 MPCore processor-based platform enables system architects and embedded software developers to apply a combination of serial and parallel processing to address the challenges they face in designing today's embedded systems, which must meet ever-growing demands to perform highly complex functions. The Xilinx Extensible Processing Platform offers embedded systems designers a processor-centric design and development approach for achieving the compute and processing horsepower required to drive tasks involving high-speed access to real-time inputs, high-performance processing and complex digital signal processing - or any combination thereof - needed to meet their application-specific requirements, including lower cost and power.

With the consumer demand for richer content and its resultant , increasing high data bandwidth continuing to drive communications systems, coding for error control has become extraordinarily important. One way to improve the bit error rate (BER), while maintaining high data reliability, is to use an error correction technique like the Viterbi algorithm. Originally conceived by Andrew Viterbi as an error-correction scheme for noisy digital communication, the Viterbi algorithm provides an efficient method for forward error correction (FEC) that improves channel reliability. Today, it is used in many digital communications systems in applications as diverse as CDMA and GSM digital cellular, dial-up modems, satellite, deep-space communications and 802.11 wireless LANs. It is also commonly used in speech recognition, keyword spotting and computational linguistics.

In many instances, the way embedded software is structured and how it interacts with the hardware in a system can have a profound effect on the transient immunity performance of a system. It can be impractical and costly to completely eliminate transients at the hardware level, so the system and software designers should plan for the occasional erroneous signal or power glitch that could cause the software to perform erratically. Erratic actions on the part of the software can be classified into two different categories:

PC-compatible industrial computers are increasing in computing power at a rapid rate due to the availability of multi-core microprocessor chips, and Microsoft Windows has become the de-facto software platform for implementing human-machine interfaces (HMIs). PCs are also becoming more reliable. With these trends, the practice of building robotic systems as complex multi-architecture, multi-platform systems is being challenged. It is now becoming possible to integrate all the functions of machine control and HMI into a single platform, without sacrificing performance and reliability of processing. Through new developments in software, we are seeing industrial systems evolving to better integrate Windows with real-time functionality such as machine vision and motion control. Software support to simplify motion control algorithm implementation already exists for the Intel processor architecture.

Supporting the Wind River Systems VxWorks operating system on Intel architecture is valuable for customers who would like to preserve their application software and evaluate other architectures for comparison purposes or for architecture conversion. Products from Wind River Systems, particularly VxWorks, offer a great opportunity to address multiple architectures with the same real-time operating system (RTOS). Furthermore, the continuous development and migration of multicore platforms has increased the focus on migrating software to multicore and performance optimizations given a specified software workload and certain hardware resources.

While the topic of timing has already been raised in previous parts in this series, the discussion here will be expanded to include the execution and response time of the software functions. When discussing timing in embedded software, there are typically two types of timing requirements, rate of execution and response time. Rate of execution deals with the event-to-event timing within a software function. It can be the timing between changes in an output, time between samples of an input, or some combination of both.

TTE Systems' RapidiTTy IDE provides a self-contained environment for developers who wish to create "time-triggered" microcontroller software in order to improve overall system reliability. RapidiTTy (Figure 1, below) is intended to address deeply-embedded applications including control and monitoring operations in medical, defence, automotive and industrial sectors, as well as in white and brown goods.

The goal of Long Term Evolution (LTE) is to achieve higher data rates through more efficient transmission, and thus improving the cellular phone user experience by enabling powerful new devices. The changes required in this technology present new challenges for base station vendors and their suppliers. Supporting 4G systems efficiently requires a number of innovations in DSP design; these innovations are moving the industry toward SoC architectures to support such systems. This paper will explore how TI's new architecture enables the key features in 4G systems.

Scientists have great expectations that nanotechnologies will bring them closer to the goal of creating computer systems that can simulate and emulate the brain's abilities for sensation, perception, action, interaction and cognition while rivaling its low power consumption and compact size. DARPA for instance, the U.S. military's research outfit known for projects that are pushing the envelope on what is technologically possible, has a program called SyNAPSE that is trying to develop electronic neuromorphic machine technology that scales to biological levels. Started in late 2008 and funded with $4.9 million, the goal of the initial phase of the SyNAPSE project is to "develop nanometer scale electronic synaptic components capable of adapting the connection strength between two neurons in a manner analogous to that seen in biological systems, as well as, simulate the utility of these synaptic components in core microcircuits that support the overall system architecture."

In then the past few years we have seen multiprocessing systems become more mainstream, in fact most modern personal computer CPUs now feature symmetric multiprocessing systems (SMP), where multiple instantiations of the same processor share the processing burden of the applications running on the PC. While SMPs are quite common today, we typically have not seen a shift towards multiprocessing in embedded computing. However, a new type of embedded design technique gives engineers the freedom to intelligently distribute processing functions across a digital subsystem. This article will look at an example of the distributed processing technique using Cypress Semiconductor's PSoC 3 and PSoC 5 architectures, which consist of a main CPU (in this case an 8051 or ARM Cortex M3), a DMA engine, and array of Universal Digital Blocks (UDB).

The consumer handheld market is growing by leaps and bounds. With more processing power and increased support for more applications, portable products are cross-pollinating with traditional computing systems even as the product life cycle has decreased considerably in this market segment. As a result, especially in this era of economic slowdown, it is imperative that new products meet the time-to-market window to gain maximum acceptance. A decrease in product life cycles requires a reduced development cycle and an increased emphasis on reusability and reprogrammability. The emerging handheld market is also seeing interesting trends in which each individual device in a family has lower volumes but there is more customization across the series of devices, effectively upping the total unit volumes. The key challenge then becomes how to develop a system that is widely reusable and also customizable. These requirements have led designers increasingly to turn to the FPGA for handheld-product development. The FPGA has become more powerful and feature-rich, while gate counts, area and frequency have increased. FPGA development and turnaround cycles are considerably shorter than those of custom ASICs, and the added advantage of reprogrammability can make the FPGA a more compelling solution for handheld embedded systems.