Digital signal processor

A digital signal processor (DSP) is a specialized microprocessor (or a SIP block), with its architecture optimized for the operational needs of digital signal processing.
The goal of DSPs is usually to measure, filter or compress continuous real-world analog signals. Most general-purpose microprocessors can also execute digital signal processing algorithms successfully, but dedicated DSPs usually have better power efficiency thus they are more suitable in portable devices such as mobile phones because of power consumption constraints. DSPs often use special memory architectures that are able to fetch multiple data or instructions at the same time.

Overview

Digital signal processing algorithms typically require a large number of mathematical operations to be performed quickly and repeatedly on a series of data samples. Signals (perhaps from audio or video sensors) are constantly converted from analog to digital, manipulated digitally, and then converted back to analog form. Many DSP applications have constraints on latency; that is, for the system to work, the DSP operation must be completed within some fixed time, and deferred (or batch) processing is not viable.
Most general-purpose microprocessors and operating systems can execute DSP algorithms successfully, but are not suitable for use in portable devices such as mobile phones and PDAs because of power efficiency constraints.A specialized digital signal processor, however, will tend to provide a lower-cost solution, with better performance, lower latency, and no requirements for specialised cooling or large batteries.
Such performance improvements have led to the introduction of digital signal processing in commercial communications satellites where hundreds or even thousands of analogue filters, switches, frequency converters and so on are required to receive and process the uplinked signals and ready them for downlinking, and can be replaced with specialised DSPs with a significant benefits to the satellites weight, power consumption, complexity/cost of construction, reliability and flexibility of operation. For example, the SES-12 and SES-14 satellites from operator SES, both intended for launch in 2017, are being built by Airbus Defence and Space with 25% of capacity using DSP.
The architecture of a digital signal processor is optimized specifically for digital signal processing. Most also support some of the features as an applications processor or microcontroller, since signal processing is rarely the only task of a system. Some useful features for optimizing DSP algorithms are outlined below.

Architecture

Software architecture

By the standards of general-purpose processors, DSP instruction sets are often highly irregular; while traditional instruction sets are made up of more general instructions that allow them to perform a wider variety of operations, instruction sets optimized for digital signal processing contain instructions for common mathematical operations that occur frequently in DSP calculations. Both traditional and DSP-optimized instruction sets are able to compute any arbitrary operation but an operation that might require multiple ARM or x86 instructions to compute might require only one instruction in a DSP optimized instruction set.
One implication for software architecture is that hand-optimized assembly-code routines are commonly packaged into libraries for re-use, instead of relying on advanced compiler technologies to handle essential algorithms.^{[clarification} Even with modern compiler optimizations hand-optimized assembly code is more efficient and many common algorithms involved in DSP calculations are hand-written in order to take full advantage of the architectural optimizations.

Instruction sets

• multiply–accumulates (MACs, including fused multiply–add, FMA) operations
  • used extensively in all kinds of matrix operations
    • convolution for filtering
    • dot product
    • polynomial evaluation
  • Fundamental DSP algorithms depend heavily on multiply–accumulate performance
    • FIR filters
    • Fast Fourier transform (FFT)
• 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.
• Multiple arithmetic units may require memory architectures to support several accesses per instruction cycle
• Special loop controls, such as architectural support for executing a few instruction words in a very tight loop without overhead for instruction fetches or exit testing

Data instructions

• 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

Program flow

• Floating-point unit integrated directly into the datapath
• Pipelined architecture
• Highly parallel multiplier–accumulators (MAC units)
• Hardware-controlled looping, to reduce or eliminate the overhead required for looping operations

Hardware architecture

In engineering, hardware architecture refers to the identification of a system's physical components and their interrelationships. This description, often called a hardware design model, allows hardware designers to understand how their components fit into a system architecture and provides to software component designers important information needed for software development and integration. Clear definition of a hardware architecture allows the various traditional engineering disciplines (e.g., electrical and mechanical engineering) to work more effectively together to develop and manufacture new machines, devices and components.
Hardware is also an expression used within the computer engineering industry to explicitly distinguish the (electronic computer) hardware from the software that runs on it. But hardware, within the automation and software engineering disciplines, need not simply be a computer of some sort. A modern automobile runs vastly more software than the Apollo spacecraft. Also, modern aircraft cannot function without running tens of millions of computer instructions embedded and distributed throughout the aircraft and resident in both standard computer hardware and in specialized hardware components such as IC wired logic gates, analog and hybrid devices, and other digital components. The need to effectively model how separate physical components combine to form complex systems is important over a wide range of applications, including computers, personal digital assistants (PDAs), cell phones, surgical instrumentation, satellites, and submarines.

Memory architecture

DSPs are usually optimized for streaming data and use special memory architectures that are able to fetch multiple data or instructions at the same time, such as the Harvard architecture or Modified von Neumann architecture, which use separate program and data memories (sometimes even concurrent access on multiple data buses).
DSPs can sometimes rely on supporting code to know about cache hierarchies and the associated delays. This is a tradeoff that allows for better performance. In addition, extensive use of DMA is employed.