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Home > Applications > DSP > Historical DSP Applications
DSP Block Diagram
DSPower-Block Diagram software was a Signalogic DSP tool product in the 1990s
DSP Block Diagram showing generated source code
In the early days of DSP, it was enough to generate run-time code from diagrams. Over time, as embedded software became more powerful -- concurrent threads, asynchronous data transfers, highly complex algorithms -- a diagrammatic interface was no longer sufficient

Historical DSP Applications

Note: In the DSP theory and background explanations below, some material is Copyright © 1996-1999 from the Motorola website, some material is Copyright © 1996-1999 from the Texas Instruments website, and some is Copyright © 1991-1999 from the Hypersignal Users Guide.

What is DSP?

In the strict sense of the term, digital signal processing refers to the electronic processing of signals such as sound, radio, and microwaves. In practice, the same characteristics that make Digital Signal Processors (DSPs) good at handling signals make them suitable for many other purposes, such as high-quality graphics processing and engineering simulations. DSPs are essentially fast number-crunchers which also happen to be small, fairly low-cost, and with fairly low-power consumption. Any place you need speed, but could not put a Pentium because it is just too darn big and needs a heat-sink and fan to keep it from melting, is a good candidate for one or more DSPs. Just about any embedded product application that involves rapid numeric processing is a candidate for a DSP.

From Analog to Digital

Classical DSP applications work with real-world signals, such as sound and radio waves that originate in analog form. Analog means the signals are continuous; they change smoothly from one state to another. Digital computers, on the other hand, treat information discontinuously, as a discrete series of binary numbers. This permits an exactness of measurement and control impossible in analog systems. The goal of digital signal processing is to use the power of digital computation to manage and modify the signal data. Therefore, the first stage in many DSP systems is to translate smooth real-world signals into a "bumpy" digital approximation. While a sound wave can be depicted as an undulating line, its digital representation looks more like an ascending and descending staircase. This translation is accomplished by an Analog-to-Digital Converter (ADC). In essence, ADCs work like a movie camera, clicking off a series of snapshots that, when strung together, approximate the continuous flow of actual events. The "snapshots" taken by ADCs are actually a series of voltage measurements that trace the rise and fall of the analog signal, like points in a connect-the-dots drawing. If the ADC has done its job well, the data points give a detailed and accurate rendering of the signal. After a certain amount of clean-up work (to remove extraneous frequencies, among other things), the ADC passes its digitized signal information to a DSP, which does the bulk of the processing. Eventually, when the DSP has finished its chores, the digital data may be turned back into an analog signal, albeit one that is quite different from and much improved over the original. For instance, a DSP can filter the noise from a signal, remove unwanted interference, amplify certain frequencies and suppress others, encrypt information, or analyze a complex wave form into its spectral components. In plainer language, DSPs can clean the crackle and hiss from music recordings, remove the echo from communications lines, make internal organs stand out more clearly in medical CAT scans, scramble cellular phone conversations to protect privacy, and assess seismic data to pinpoint new oil reserves. Of course there are also DSP applications that don't require Analog-to-Digital translation. The data is digital from the start, and can be manipulated directly by the DSP. An example of this is computer graphics where DSPs create mathematical models of things like weather systems, images and scientific simulations.

Blinding Speed

At its heart, digital signal processing is highly numerical and very repetitive. As each new piece of signal data arrives, it must be multiplied, summed, and otherwise transformed according to complex formulas. What makes this such a keen technological challenge is the speed requirement. DSP systems must work in real time, capturing and processing information as it happens. Like a worker on a fast-moving assembly line, Analog-to-Digital converters and DSPs must keep up with the work flow. If they fall behind, information is lost and the signal gets distorted. The Analog-to-Digital converter, for instance, must take its signal samples often enough to catch all the relevant fluctuations. If the ADC is too slow, it misses some of the action. Imagine trying to film a football game with a movie camera running at one frame per minute. The film would be incoherent, missing entire plays in the intervals between frames. The DSP, too, must keep pace, churning out calculations as fast as the signal data is received from the ADC. The pace gets progressively more demanding as the signal gets faster. Stereo equipment handles sound signals of up to 20 kilohertz (20,000 cycles per second, the upper limit of human hearing), requiring a DSP to perform hundreds of millions of operations per second. Other signals, such as satellite transmissions, are even faster, reaching up into the Gigahertz (billions of cycles per second) range.

DSPs versus Microprocessors

DSPs differ from microprocessors in a number of ways. Microprocessors are typically built for a range of general purpose functions, and normally run large blocks of software, such as operating systems like Windows or UNIX. Although today's microprocessors, including the popular and well-known Pentium family, are extremely fast--as fast or faster than some DSPs--they are still not often called upon to perform real-time computation or signal processing. Usually, their bulk processing power is directed more at handling many tasks at once, and controlling huge amounts of memory and data, and controlling a wide variety of computer peripherals (disk drive, modem, video display, etc). However, microprocessors such as Pentiums are notorious for their size, cost, and power consumption to achieve their muscular performance, whereas DSPs are more dedicated, racing through a smaller range of functions at lightning speed, yet less costly and requiring much less space (size) and power consumption to achieve their purpose. DSPs are often used in "embedded systems", where they are accompanied by all necessary software (stored in onchip ROM or offchip EEPROM), built deep into a piece of equipment, and dedicated to a group of related tasks. In computer systems, DSPs may be employed as attached processors, assisting a general purpose host microprocessor.

Different DSPs For Different Jobs

One way to classify DSP devices and applications is by their dynamic range. The dynamic range is the spread of numbers, from small to large, that must be processed in the course of an application. It takes a certain range of values, for instance, to describe the entire waveform of a particular signal, from deepest valley to highest peak. The range may get even wider as calculations are performed, generating larger and smaller numbers through multiplication and division. The DSP device must have the capacity to handle the numbers so generated. If it doesn't, the numbers may "overflow," producing invalid results. The processor's capacity is a function of its data width (i.e. the number of bits it manipulates) and the type of arithmetic it performs (i.e., fixed or floating point). A 32-bit processor has a wider dynamic range than a 24-bit processor, which has a wider range than 16-bit processor. And floating-point chips have wider ranges than fixed-point devices. Each type of processor is suited for a particular range of applications. 16-bit fixed-point DSPs such as typically used for voice-grade and telecom systems (such as cell-phones), since they work with a relatively narrow range of sound frequencies. On the other hand, hi-fidelity stereo sound has a wider range, calling for a 16-bit ADC or 24-bit ADC, and a 24-bit fixed-point DSP like the Motorola DSP563xx series. In this case, the ADC's 16-bit or 24-bit width is needed to capture the complete high-fidelity signal (i.e. much better than a phone); the DSP thus must be 24 bits to accommodate the larger values resulting when the signal data is manipulated.) Applications requiring still greater dynamic range include image processing, 3-D graphics, and scientific and research simulations; such applications typically a 32-bit floating-point processor.

DSP Evolution

Just a decade and a half ago, digital signal processing was more theory than practice. The only systems capable of doing signal processing were massive mainframes and supercomputers and even then, much of the processing was done not in real time, but off-line in batches. For example, seismic data was collected in the field, stored on magnetic tapes and then taken to a computing center, where a mainframe might take hours or days to digest the information. The first practical real-time DSP systems emerged in the late 1970s and used bipolar "bit-slice" components. Large quantities of these building-block chips were needed to design a system, at considerable effort and expense. Uses were limited to esoteric high-end technology, such as military and space systems. The economics began to change in the early 80s with the advent of single-chip MOS (metal-Oxide Semiconductor) DSPs. Cheaper and easier to design-in than building blocks, these "monolithic" processors meant that digital signal processing could be cost-effectively integrated into an array of ordinary products. The early single-chip processors were relatively simple 16-bit devices, which, teamed with 8- or 10-bit ADCs, were suitable for low-speed applications, general-purpose coders such as talking toys, simple controllers, and vocoders; (voice encoding devices used in telecommunications).

Things that have DSPs

Some typical and well-known items which contain one (or many) embedded DSPs:

Applications Using Signalogic DSP Products: Some Examples

As Signalogic has grown and matured beyond basic DSP development tool products, we have found ourselves increasingly involved in customer-specific applications of DSP. Besides company growth, from these deeper involvements comes improvement to our DSP development tools, new DSP products, and increased knowledge of the overall DSP field. Some recent examples of applications in which Signalogic has been a co-development partner:

Emerging application: DSP-based Voice-over-Internet Protocol

Voice-over-Internet Protocol (VoIP) is one example that will benefit from TI's DSPs, particulary the new 'C54xx devices.

Shifting voice traffic from traditional telephone networks to well-managed IP-based data networks is a growing trend that provides advantges such as system efficiency and lower calling costs.

DSPs are at the core of the voice and data networks infrastructure, and TI, the world leader in DSPs, is also the DSP leader in the VoIP market. A DSP-based VoIP system or gateway makes a shift to data networks possible, serving as the bridge between the public switched telephone network (PSTN) and the packet network. VoIP gateways allow users to speak on regular phones or send information over regular fax machines as they bypass PSTN toll charges with no perceivable loss of quality.

DSP advancements in processing horsepower, smaller footprint and reductions in power dissipation have expanded the number of channels carried on VoIP gateways and embedded in network backbones. These advancements are transforming a technology, once used primarily to obtain free phone calls through a PC and the public Internet.