10-06-2017, 07:12 AM
Modern DSPs
Modern signal processors yield greater performance; this is due in part to both technological and architectural advancements like lower design rules, fast-access two-level cache, (E)DMA circuitry and a wider bus system. Not all DSP's provide the same speed and many kinds of signal processors exist, each one of them being better suited for a specific task, ranging in price from about US$1.50 to US$300
Texas Instruments produces the C6000 series DSP s, which have clock speeds of 1.2 GHz and implement separate instruction and data caches. They also have an 8 MiB 2nd level cache and 64 EDMA channels. The top models are capable of as many as 8000 MIPS (instructions per second), use VLIW (very long instruction word), perform eight operations per clock-cycle and are compatible with a broad range of external peripherals and various buses (PCI/serial/etc). TMS320C6474 chips each have three such DSP's, and the newest generation C6000 chips support floating point as well as fixed point processing.
Freescale produce a multi-core DSP family, the MSC81xx. The MSC81xx is based on StarCore Architecture processors and the latest MSC8144 DSP combines four programmable SC3400 StarCore DSP cores. Each SC3400 StarCore DSP core has a clock speed of 1 GHz.
Analog Devices produce the SHARC-based DSP and range in performance from 66 MHz/198 MFLOPS (million floating-point operations per second) to 400 MHz/2400 MFLOPS. Some models support multiple multipliers and ALUs, SIMD instructions and audio processing-specific components and peripherals. The Blackfin family of embedded digital signal processors combine the features of a DSP with those of a general use processor. As a result, these processors can run simple operating systems like CLinux, velOSity and Nucleus RTOS while operating on real-time data.
NXP Semiconductors produce DSP's based on TriMedia VLIW technology, optimized for audio and video processing. In some products the DSP core is hidden as a fixed-function block into a SoC, but NXP also provides a range of flexible single core media processors. The TriMedia media processors support both fixed-point arithmetic as well as floating-point arithmetic, and have specific instructions to deal with complex filters and entropy coding.
Most DSP's use fixed-point arithmetic, because in real world signal processing the additional range provided by floating point is not needed, and there is a large speed benefit and cost benefit due to reduced hardware complexity. Floating point DSP's may be invaluable in applications where a wide dynamic range is required. Product developers might also use floating point DSP's to reduce the cost and complexity of software development in exchange for more expensive hardware, since it is generally easier to implement algorithms in floating point.
Generally, DSP's are dedicated integrated circuits; however DSP functionality can also be produced by using field-programmable gate array chips (FPGA s).
Embedded general-purpose RISC processors are becoming increasingly DSP like in functionality. For example, the ARM Cortex-A8 and the OMAP3 processors include a Cortex-A8 and C6000 DSP.
Preface
Digital signal processing (DSP) is one of the fastest-growing fields in modern electronics. Only a few years ago DSP techniques were considered advanced and esoteric subjects, their use limited to
research labs or advanced applications such as radar identification. Today, the technology has found its way into virtually every segment of electronics. Talking toys, computer graphics, and CD players are just a few of the common examples. The rapid acceptance and commercialization of this technology has presented the modern design engineer with a serious challenge:
either gain a working knowledge of the new techniques or risk
obsolescence. Unfortunately, anyone attempting to gain this
knowledge has had to face some serious obstacles. Traditionally,
engineers have had two options for acquiring new skills: go back
to school, or turn to vendor s technical documentation.
The Need for DSP
What is digital signal processing (DSP) and why should we use it? Before discussing either the hardware, the software, or the underlying mathematics, it s a good idea to answer these basic questions.
The term DSP generally refers to the use of digital computers to process signals. Normally, these signals can be handled by analog processes but, for a variety of reasons, we may prefer to handle them digitally. To understand the relative merits of analog and digital processing, it is convenient to compare the two techniques in a common application.
There are two distinct disadvantages to the digital process: first, it is far more complicated than the analog process; second, computers can only handle numbers of finite resolution. Thus, the (potentially) infinite resolution of the analog signal is lost.
Advantages of DSP
Obviously, there must be some compensating benefits of the
digital process, and indeed there are. First, once converted to numbers,
the signal is unconditionally stable. Using techniques such as
error detection and correction, it is possible to store, transmit, and
reproduce numbers with no corruption. The twentieth generation
of recording is therefore just as accurate as the first generation This fact has some interesting implications. Future generations
will never really know what the Beatles sounded like, for example.
The commercial analog technology of the 1960s was simply not
able to accurately record and reproduce the signals. Several generations
of analog signals were needed to reproduce the sound: First,
a master tape would be recorded, and then mixed and edited; from
this, a metal master record would be produced, from which would
come a plastic impression. Each step of the process was a new
generation of recording, and each generation acted on the signal
like a filter, reducing the frequency content and skewing the phase.
As with the paintings in the Sistine Chapel, the true colors and
brilliance of the original art is lost to history.
Things are different for today s musicians. A thousand years
from now historians will be able to accurately play back the digitally
mastered CDs of today. The discs themselves may well deteriorate,
but before they do, the digital numbers on them can be copied with
perfect accuracy. Signals stored digitally are really just large arrays
of numbers. As such, they are immune to the physical limitations of
analog signals.
There are other significant advantages to processing signals
digitally. Geophysicists were one of the first groups to apply the
techniques of signal processing. The seismic signals of interest to
them are often of very low frequency, from 0.01 Hz to 10 Hz. It is
difficult to build analog filters that work at these low frequencies.
Component values must be so large that physically implementing
the filter may well be impossible. Once the signals have been
converted to digital numbers, however, it is a straightforward
process to program a computer to perform the filtering.
Other advantages to digital signals abound. For example, DSP
can allow large bandwidth signals to be sent over narrow bandwidth
Introduction
The general model for a DSP system is shown in Figure 2-1.
From a high-level point of view, a DSP system performs the following
operations:
Accepts an analog signal as an input.
Converts this analog signal to numbers.
Performs computations using the numbers.
Converts the results of the computations back into an
analog signal.
Optionally, different types of information can be derived from
the numbers used in this process. This information may be analyzed,
stored, displayed, transmitted, or otherwise manipulated.
This model can be rearranged in several ways. For example, a
CD player will not have the analog input section. A laboratory
instrument may not have the analog output. The truly amazing
thing about DSP systems, however, is that the model will fit any
DSP application. The system could be a sonar or radar system,
voicemail system, video camera, or a host of other applications.
The specifications of the individual key elements may change,
but their function will remain the same.
In order to understand the overall DSP system, let s begin with
a qualitative discussion of the key elements.
Input
All signal processing begins with an input transducer. The input
transducer takes the input signal and converts it to an electrical
signal. In signal-processing applications, the transducer can take
many forms. A common example of an input transducer is a microphone.
Other examples are geophones for seismic work, radar
antennas, and infrared sensors. Generally, the output of the transducer
is quite small: a few microvolts to several millivolts.
Signal-conditioning Circuit
The purpose of the signal-conditioning circuit is to take the
few millivolts of output from the input transducer and convert it
to levels usable by the following stages. Generally, this means
amplifying the signal to somewhere between 3 and 12V. The signalconditioning
circuit also limits the input signal to prevent damage to following stages. In some circuits, the conditioning circuit provides
isolation between the transducer and the rest of the system
circuitry.
Typically, signal-conditioning circuits are based on operational
amplifiers or instrumentation amplifiers.
Anti-aliasing Filter
The anti-aliasing filter is a low-pass filter. The job of the antialiasing
filter is a little difficult to describe without more theoretical
background than we have developed up to this point (see Chapter 6
for more details). However, from a conceptual point of view, the
anti-aliasing filter can be thought of as a mechanism to limit how
fast the input signal can change. This is a critical function; the antialiasing
filter ensures that the rest of the system will be able to track
the signal. If the signal changes too rapidly, the rest of the system
could miss critical parts of the signal.
Analog-to-Digital Converter
As the name implies, the purpose of the analog-to-digital
converter (ADC) is to convert the signal from its analog form to
a digital data representation. Due to the physics of converter circuitry,
most ADCs require inputs of at least several volts for their
full range input. Two of the most important characteristics of an
ADC are the conversion rate and the resolution. The conversion rate
defines how fast the ADC can convert an analog value to a digital
value. The resolution defines how close the digital number is to the
actual analog value.
The output of the ADC is a binary number that can be manipulated
mathematically.
Processor
Theoretically, there is nothing special about the processor. It
simply performs the calculations required for processing the signal.
For example, if our DSP system is a simple amplifier, then the input
value is literally multiplied by the gain (amplification) constant.
In the early days of signal processing, the processor was often
a general-purpose mainframe computer. As the field of DSP progressed,
special high-speed processors were designed to handle the
number crunching.
Today, a wide variety of specialized processors are dedicated
to DSP. These processors are designed to achieve very high data
throughputs, using a combination of high-speed hardware, specialized
architectures, and dedicated instruction sets. All of these
functions are designed to efficiently implement DSP algorithms.
Program Store, Data Store
The program store stores the instructions used in implementing
the required DSP algorithms. In a general-purpose computer (von
Neumann architecture), data and instructions are stored together.
In most DSP systems, the program is stored separately from the
data, since this allows faster execution of the instructions. Data
can be moved on its own bus at the same time that instructions are
being fetched. This architecture was developed from basic research
performed at Harvard University, and therefore is generally called
a Harvard architecture. Often the data bus and the instruction bus
have different widths.
Data Transmission
DSP data is commonly transmitted to other DSP systems.
Sometimes the data is stored in bulk form on magnetic tape, optical discs (CDs), or other media. This ability to store and transmit the
data in digital form is one of the key benefits of DSP operations.
An analog signal, no matter how it is stored, will immediately begin
to degrade. A digital signal, however, is much more robust since it is
composed of ones and zeroes. Furthermore, the digital signal can be
protected with error detection and correction codes.
Display and User Input
Not all DSP systems have displays or user input. However, it is
often handy to have some visual representation of the signal. If the
purpose of the system is to manipulate the signal, then obviously
the user needs a way to input commands to the system. This can be
accomplished with a specialized keypad, a few discrete switches, or
a full keyboard.
Digital-to-Analog Converter
In many DSP systems, the signal must be converted back to
analog form after it has been processed. This is the function of the
digital-to-analog converter (DAC). Conceptually, DACs are quite
straightforward: a binary number put on the input causes a corresponding
voltage on the output. One of the key specifications of
the DAC is how fast the output voltage settles to the commanded
value. The slew rate of the DAC should be matched to the acquisition
rate of the ADC.
Output Smoothing Filter
As the name implies, the purpose of the smoothing filter is
to take the edges off the waveform coming from the DAC. This
is necessary since the waveform will have a stair-step shape,
resulting from the sequence of discrete inputs applied to the DAC Generally, the smoothing filter is a simple low-pass system. Often, a
basic RC circuit does the job.
Output Amplifier
The output amplifier is generally a straightforward amplifier
with two main purposes. First, it matches the high impedance of
the DAC to the low impedance of the transducer. Second, it boosts
the power to the level required.
Output Transducer
Like the input transducer, the output transducer can assume
a variety of forms. Common examples are speakers, antennas, and
motors.
Modern signal processors yield greater performance; this is due in part to both technological and architectural advancements like lower design rules, fast-access two-level cache, (E)DMA circuitry and a wider bus system. Not all DSP's provide the same speed and many kinds of signal processors exist, each one of them being better suited for a specific task, ranging in price from about US$1.50 to US$300
Texas Instruments produces the C6000 series DSP s, which have clock speeds of 1.2 GHz and implement separate instruction and data caches. They also have an 8 MiB 2nd level cache and 64 EDMA channels. The top models are capable of as many as 8000 MIPS (instructions per second), use VLIW (very long instruction word), perform eight operations per clock-cycle and are compatible with a broad range of external peripherals and various buses (PCI/serial/etc). TMS320C6474 chips each have three such DSP's, and the newest generation C6000 chips support floating point as well as fixed point processing.
Freescale produce a multi-core DSP family, the MSC81xx. The MSC81xx is based on StarCore Architecture processors and the latest MSC8144 DSP combines four programmable SC3400 StarCore DSP cores. Each SC3400 StarCore DSP core has a clock speed of 1 GHz.
Analog Devices produce the SHARC-based DSP and range in performance from 66 MHz/198 MFLOPS (million floating-point operations per second) to 400 MHz/2400 MFLOPS. Some models support multiple multipliers and ALUs, SIMD instructions and audio processing-specific components and peripherals. The Blackfin family of embedded digital signal processors combine the features of a DSP with those of a general use processor. As a result, these processors can run simple operating systems like CLinux, velOSity and Nucleus RTOS while operating on real-time data.
NXP Semiconductors produce DSP's based on TriMedia VLIW technology, optimized for audio and video processing. In some products the DSP core is hidden as a fixed-function block into a SoC, but NXP also provides a range of flexible single core media processors. The TriMedia media processors support both fixed-point arithmetic as well as floating-point arithmetic, and have specific instructions to deal with complex filters and entropy coding.
Most DSP's use fixed-point arithmetic, because in real world signal processing the additional range provided by floating point is not needed, and there is a large speed benefit and cost benefit due to reduced hardware complexity. Floating point DSP's may be invaluable in applications where a wide dynamic range is required. Product developers might also use floating point DSP's to reduce the cost and complexity of software development in exchange for more expensive hardware, since it is generally easier to implement algorithms in floating point.
Generally, DSP's are dedicated integrated circuits; however DSP functionality can also be produced by using field-programmable gate array chips (FPGA s).
Embedded general-purpose RISC processors are becoming increasingly DSP like in functionality. For example, the ARM Cortex-A8 and the OMAP3 processors include a Cortex-A8 and C6000 DSP.
Preface
Digital signal processing (DSP) is one of the fastest-growing fields in modern electronics. Only a few years ago DSP techniques were considered advanced and esoteric subjects, their use limited to
research labs or advanced applications such as radar identification. Today, the technology has found its way into virtually every segment of electronics. Talking toys, computer graphics, and CD players are just a few of the common examples. The rapid acceptance and commercialization of this technology has presented the modern design engineer with a serious challenge:
either gain a working knowledge of the new techniques or risk
obsolescence. Unfortunately, anyone attempting to gain this
knowledge has had to face some serious obstacles. Traditionally,
engineers have had two options for acquiring new skills: go back
to school, or turn to vendor s technical documentation.
The Need for DSP
What is digital signal processing (DSP) and why should we use it? Before discussing either the hardware, the software, or the underlying mathematics, it s a good idea to answer these basic questions.
The term DSP generally refers to the use of digital computers to process signals. Normally, these signals can be handled by analog processes but, for a variety of reasons, we may prefer to handle them digitally. To understand the relative merits of analog and digital processing, it is convenient to compare the two techniques in a common application.
There are two distinct disadvantages to the digital process: first, it is far more complicated than the analog process; second, computers can only handle numbers of finite resolution. Thus, the (potentially) infinite resolution of the analog signal is lost.
Advantages of DSP
Obviously, there must be some compensating benefits of the
digital process, and indeed there are. First, once converted to numbers,
the signal is unconditionally stable. Using techniques such as
error detection and correction, it is possible to store, transmit, and
reproduce numbers with no corruption. The twentieth generation
of recording is therefore just as accurate as the first generation This fact has some interesting implications. Future generations
will never really know what the Beatles sounded like, for example.
The commercial analog technology of the 1960s was simply not
able to accurately record and reproduce the signals. Several generations
of analog signals were needed to reproduce the sound: First,
a master tape would be recorded, and then mixed and edited; from
this, a metal master record would be produced, from which would
come a plastic impression. Each step of the process was a new
generation of recording, and each generation acted on the signal
like a filter, reducing the frequency content and skewing the phase.
As with the paintings in the Sistine Chapel, the true colors and
brilliance of the original art is lost to history.
Things are different for today s musicians. A thousand years
from now historians will be able to accurately play back the digitally
mastered CDs of today. The discs themselves may well deteriorate,
but before they do, the digital numbers on them can be copied with
perfect accuracy. Signals stored digitally are really just large arrays
of numbers. As such, they are immune to the physical limitations of
analog signals.
There are other significant advantages to processing signals
digitally. Geophysicists were one of the first groups to apply the
techniques of signal processing. The seismic signals of interest to
them are often of very low frequency, from 0.01 Hz to 10 Hz. It is
difficult to build analog filters that work at these low frequencies.
Component values must be so large that physically implementing
the filter may well be impossible. Once the signals have been
converted to digital numbers, however, it is a straightforward
process to program a computer to perform the filtering.
Other advantages to digital signals abound. For example, DSP
can allow large bandwidth signals to be sent over narrow bandwidth
Introduction
The general model for a DSP system is shown in Figure 2-1.
From a high-level point of view, a DSP system performs the following
operations:
Accepts an analog signal as an input.
Converts this analog signal to numbers.
Performs computations using the numbers.
Converts the results of the computations back into an
analog signal.
Optionally, different types of information can be derived from
the numbers used in this process. This information may be analyzed,
stored, displayed, transmitted, or otherwise manipulated.
This model can be rearranged in several ways. For example, a
CD player will not have the analog input section. A laboratory
instrument may not have the analog output. The truly amazing
thing about DSP systems, however, is that the model will fit any
DSP application. The system could be a sonar or radar system,
voicemail system, video camera, or a host of other applications.
The specifications of the individual key elements may change,
but their function will remain the same.
In order to understand the overall DSP system, let s begin with
a qualitative discussion of the key elements.
Input
All signal processing begins with an input transducer. The input
transducer takes the input signal and converts it to an electrical
signal. In signal-processing applications, the transducer can take
many forms. A common example of an input transducer is a microphone.
Other examples are geophones for seismic work, radar
antennas, and infrared sensors. Generally, the output of the transducer
is quite small: a few microvolts to several millivolts.
Signal-conditioning Circuit
The purpose of the signal-conditioning circuit is to take the
few millivolts of output from the input transducer and convert it
to levels usable by the following stages. Generally, this means
amplifying the signal to somewhere between 3 and 12V. The signalconditioning
circuit also limits the input signal to prevent damage to following stages. In some circuits, the conditioning circuit provides
isolation between the transducer and the rest of the system
circuitry.
Typically, signal-conditioning circuits are based on operational
amplifiers or instrumentation amplifiers.
Anti-aliasing Filter
The anti-aliasing filter is a low-pass filter. The job of the antialiasing
filter is a little difficult to describe without more theoretical
background than we have developed up to this point (see Chapter 6
for more details). However, from a conceptual point of view, the
anti-aliasing filter can be thought of as a mechanism to limit how
fast the input signal can change. This is a critical function; the antialiasing
filter ensures that the rest of the system will be able to track
the signal. If the signal changes too rapidly, the rest of the system
could miss critical parts of the signal.
Analog-to-Digital Converter
As the name implies, the purpose of the analog-to-digital
converter (ADC) is to convert the signal from its analog form to
a digital data representation. Due to the physics of converter circuitry,
most ADCs require inputs of at least several volts for their
full range input. Two of the most important characteristics of an
ADC are the conversion rate and the resolution. The conversion rate
defines how fast the ADC can convert an analog value to a digital
value. The resolution defines how close the digital number is to the
actual analog value.
The output of the ADC is a binary number that can be manipulated
mathematically.
Processor
Theoretically, there is nothing special about the processor. It
simply performs the calculations required for processing the signal.
For example, if our DSP system is a simple amplifier, then the input
value is literally multiplied by the gain (amplification) constant.
In the early days of signal processing, the processor was often
a general-purpose mainframe computer. As the field of DSP progressed,
special high-speed processors were designed to handle the
number crunching.
Today, a wide variety of specialized processors are dedicated
to DSP. These processors are designed to achieve very high data
throughputs, using a combination of high-speed hardware, specialized
architectures, and dedicated instruction sets. All of these
functions are designed to efficiently implement DSP algorithms.
Program Store, Data Store
The program store stores the instructions used in implementing
the required DSP algorithms. In a general-purpose computer (von
Neumann architecture), data and instructions are stored together.
In most DSP systems, the program is stored separately from the
data, since this allows faster execution of the instructions. Data
can be moved on its own bus at the same time that instructions are
being fetched. This architecture was developed from basic research
performed at Harvard University, and therefore is generally called
a Harvard architecture. Often the data bus and the instruction bus
have different widths.
Data Transmission
DSP data is commonly transmitted to other DSP systems.
Sometimes the data is stored in bulk form on magnetic tape, optical discs (CDs), or other media. This ability to store and transmit the
data in digital form is one of the key benefits of DSP operations.
An analog signal, no matter how it is stored, will immediately begin
to degrade. A digital signal, however, is much more robust since it is
composed of ones and zeroes. Furthermore, the digital signal can be
protected with error detection and correction codes.
Display and User Input
Not all DSP systems have displays or user input. However, it is
often handy to have some visual representation of the signal. If the
purpose of the system is to manipulate the signal, then obviously
the user needs a way to input commands to the system. This can be
accomplished with a specialized keypad, a few discrete switches, or
a full keyboard.
Digital-to-Analog Converter
In many DSP systems, the signal must be converted back to
analog form after it has been processed. This is the function of the
digital-to-analog converter (DAC). Conceptually, DACs are quite
straightforward: a binary number put on the input causes a corresponding
voltage on the output. One of the key specifications of
the DAC is how fast the output voltage settles to the commanded
value. The slew rate of the DAC should be matched to the acquisition
rate of the ADC.
Output Smoothing Filter
As the name implies, the purpose of the smoothing filter is
to take the edges off the waveform coming from the DAC. This
is necessary since the waveform will have a stair-step shape,
resulting from the sequence of discrete inputs applied to the DAC Generally, the smoothing filter is a simple low-pass system. Often, a
basic RC circuit does the job.
Output Amplifier
The output amplifier is generally a straightforward amplifier
with two main purposes. First, it matches the high impedance of
the DAC to the low impedance of the transducer. Second, it boosts
the power to the level required.
Output Transducer
Like the input transducer, the output transducer can assume
a variety of forms. Common examples are speakers, antennas, and
motors.