What is Digital Signal Processor ?

A digital signal processor (DSP) is a specialized microprocessor designed specifically for digital signal processing, generally in real-time.

Characteristics of Digital Signal Processors

• Designed for real-time processing
• Optimum performance with streaming data
• Separate program and data memories (Harvard architecture)
• Special Instructions for SIMD (Single Instruction, Multiple Data) operations
• No hardware support for multitasking
• The ability to act as a direct memory access device if in a host environment
• Processes digital signals converted (using an Analog-to-digital converter (ADC)) from analog signals. Output is then converted back to analog form using a Digital-to-analog converter (DAC)

Architectural features of digital signal processors

Digital signal processing can be done on general-purpose microprocessors. However, a digital signal processor contains architectural optimizations to speed up processing. These optimizations are also important to lower costs, heat-emission and power-consumption.

Program flow

• Floating-point unit integrated directly into the data-path.
• Pipelined architecture
• Highly parallel accumulator and multiplier
• Special looping hardware. Low-overhead or Zero-overhead looping capability

Memory architecture

• DSPs often use special memory architectures that are able to fetch multiple data and/or instructions at the same time:
  • Harvard architecture
  • modified von Neumann architecture
• Use of direct memory access
• Memory-address calculation unit

Data operations

• Saturation arithmetic, in which operations that produce overflows will accumulate at the maximum (or minimum) values that the register can hold rather than wrapping around (maximum+1 doesn't overflow to minimum as in many general-purpose CPUs, instead it stays at maximum). Sometimes various sticky bits operation modes are available.
• Fixed-point arithmetic is often used to speed up arithmetic processing.
• Single-cycle operations to increase the benefits of pipelining.

Instruction sets

• Multiply-accumulate (MAC) operations, which are good for all kinds of matrix operations, such as convolution for filtering, dot product, or even polynomial evaluation (see Horner scheme, also fused multiply-add).
• Instructions to increase parallelism: SIMD, VLIW, superscalar architecture.
• Specialized instructions for modulo addressing in ring buffers and bit-reversed addressing mode for FFT cross-referencing.
• Digital signal processors sometimes use time-stationary encoding to simplify hardware and increase coding efficiency