Microprocessor architectures can be optimized for increased efficiency in certain applications
through the inclusion of special instructions and execution units. One major class of application-specific microprocessors is the digital signal processor, or DSP. DSP entails a microprocessor mathematically manipulating a sampled analog signal in a way that emulates transformation of that signal by discrete analog components such as filters or amplifiers. To operate on an analog signal digitally, the analog signal must be sampled by an analog-to-digital converter, manipulated, and then reconstructed with a digital-to-analog converter. A rough equivalency of digital signal processing versus conventional analog transformation. In this example of a lowpass filter (the amplitude of frequencies above a certain threshold are attenuated), the complexity of digital sampling and a microprocessor appears unjustified. The power of DSP comes when much more complex analog transformations are performed that would require excessively complex analog circuit topologies. Some examples of applications in which DSPs are used include modems, cellular telephones, and radar. While sensitive analog circuits may degrade or fall out of calibration over time, digital instructions and sequences maintain their integrity indefinitely.
Major manufacturers of DSPs include Analog Devices, Motorola, and Texas Instruments.
Many books have been written on DSP algorithms and techniques, which are extremely diverse and challenging topics. DSP algorithms are characterized by repetitive multiplication and addition operations carried out on the sampled data set. Multiply and addition operations are also known as multiply and accumulate, or MAC, operations in DSP parlance. These calculations involve the sampled data as well as coefficients that, along with the specific operations, define the transformation being performed. For DSP to be practical, it must be performed in real time, because the signals cannot be paused while waiting for the microprocessor to finish its previous operation. For DSP to be economical, this throughput must be achieved at an acceptable cost. A general-purpose microprocessor can be used to perform DSP functions, but in most cases, the solution will not be economical. This is because the microprocessor is designed to execute general programs for which there is less emphasis on specific types of calculations. A DSP is designed specifically to rapidly execute multiply and accumulate operations, and it contains additional hardware to efficiently fetch sequential operands from tables in memory. Not all of the features discussed below are implemented by all DSPs, but they are presented to provide an understanding of the overall set of characteristics that differentiates a DSP from
a generic microprocessor.
through the inclusion of special instructions and execution units. One major class of application-specific microprocessors is the digital signal processor, or DSP. DSP entails a microprocessor mathematically manipulating a sampled analog signal in a way that emulates transformation of that signal by discrete analog components such as filters or amplifiers. To operate on an analog signal digitally, the analog signal must be sampled by an analog-to-digital converter, manipulated, and then reconstructed with a digital-to-analog converter. A rough equivalency of digital signal processing versus conventional analog transformation. In this example of a lowpass filter (the amplitude of frequencies above a certain threshold are attenuated), the complexity of digital sampling and a microprocessor appears unjustified. The power of DSP comes when much more complex analog transformations are performed that would require excessively complex analog circuit topologies. Some examples of applications in which DSPs are used include modems, cellular telephones, and radar. While sensitive analog circuits may degrade or fall out of calibration over time, digital instructions and sequences maintain their integrity indefinitely.
Major manufacturers of DSPs include Analog Devices, Motorola, and Texas Instruments.
Many books have been written on DSP algorithms and techniques, which are extremely diverse and challenging topics. DSP algorithms are characterized by repetitive multiplication and addition operations carried out on the sampled data set. Multiply and addition operations are also known as multiply and accumulate, or MAC, operations in DSP parlance. These calculations involve the sampled data as well as coefficients that, along with the specific operations, define the transformation being performed. For DSP to be practical, it must be performed in real time, because the signals cannot be paused while waiting for the microprocessor to finish its previous operation. For DSP to be economical, this throughput must be achieved at an acceptable cost. A general-purpose microprocessor can be used to perform DSP functions, but in most cases, the solution will not be economical. This is because the microprocessor is designed to execute general programs for which there is less emphasis on specific types of calculations. A DSP is designed specifically to rapidly execute multiply and accumulate operations, and it contains additional hardware to efficiently fetch sequential operands from tables in memory. Not all of the features discussed below are implemented by all DSPs, but they are presented to provide an understanding of the overall set of characteristics that differentiates a DSP from
a generic microprocessor.
