Digital signal processor

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 (assembly programs) are commonly packaged into libraries for re-use, instead of relying on advanced compiler technologies to handle essential algorithms. 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.

• 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)
• related instructions:
  • SIMD
  • VLIW
• Specialized instructions for modulo addressing in ring buffers and bit-reversed addressing mode for FFT cross-referencing
• DSPs 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 – typically supporting reading 2 data values from 2 separate data buses and the next instruction (from the instruction cache, or a 3rd program memory) simultaneously.[7][8][9][10]
• 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—such as zero-overhead looping[11][12] and hardware loop buffers.[13][14]

• 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

• 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

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.

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.

DSPs frequently use multi-tasking operating systems, but have no support for virtual memory or memory protection. Operating systems that use virtual memory require more time for context switching among processes, which increases latency.

• Hardware modulo addressing
  • Allows circular buffers to be implemented without having to test for wrapping
• Bit-reversed addressing, a special addressing mode
  • useful for calculating FFTs
• Exclusion of a memory management unit
• Address generation unit

In 1976, Richard Wiggins proposed the Speak & Spell concept to Paul Breedlove, Larry Brantingham, and Gene Frantz at Texas Instruments' Dallas research facility. Two years later in 1978, they produced the first Speak & Spell, with the technological centerpiece being the TMS5100,[15] the industry's first digital signal processor. It also set other milestones, being the first chip to use linear predictive coding to perform speech synthesis.[16] The chip was made possible with a 7 µm PMOS fabrication process.[17]

In 1978, American Microsystems (AMI) released the S2811.[3][4] The AMI S2811 "signal processing peripheral", like many later DSPs, has a hardware multiplier that enables it to do multiply–accumulate operation in a single instruction.[18] The S2281 was the first integrated circuit chip specifically designed as a DSP, and fabricated using VMOS (V-groove MOS), a technology that had previously not been mass-produced.[4] It was designed as a microprocessor peripheral, for the Motorola 6800,[3] and it had to be initialized by the host. The S2811 was not successful in the market.

In 1979, Intel released the 2920 as an "analog signal processor".[19] It had an on-chip ADC/DAC with an internal signal processor, but it didn't have a hardware multiplier and was not successful in the market.

In 1980, the first stand-alone, complete DSPs – Nippon Electric Corporation's NEC µPD7720 and AT&T's DSP1 – were presented at the International Solid-State Circuits Conference '80. Both processors were inspired by the research in public switched telephone network (PSTN) telecommunications. The µPD7720, introduced for voiceband applications, was one of the most commercially successful early DSPs.[3]

The Altamira DX-1 was another early DSP, utilizing quad integer pipelines with delayed branches and branch prediction.

Another DSP produced by Texas Instruments (TI), the TMS32010 presented in 1983, proved to be an even bigger success. It was based on the Harvard architecture, and so had separate instruction and data memory. It already had a special instruction set, with instructions like load-and-accumulate or multiply-and-accumulate. It could work on 16-bit numbers and needed 390 ns for a multiply–add operation. TI is now the market leader in general-purpose DSPs.

About five years later, the second generation of DSPs began to spread. They had 3 memories for storing two operands simultaneously and included hardware to accelerate tight loops; they also had an addressing unit capable of loop-addressing. Some of them operated on 24-bit variables and a typical model only required about 21 ns for a MAC. Members of this generation were for example the AT&T DSP16A or the Motorola 56000.

The main improvement in the third generation was the appearance of application-specific units and instructions in the data path, or sometimes as coprocessors. These units allowed direct hardware acceleration of very specific but complex mathematical problems, like the Fourier-transform or matrix operations. Some chips, like the Motorola MC68356, even included more than one processor core to work in parallel. Other DSPs from 1995 are the TI TMS320C541 or the TMS 320C80.

The fourth generation is best characterized by the changes in the instruction set and the instruction encoding/decoding. SIMD extensions were added, and VLIW and the superscalar architecture appeared. As always, the clock-speeds have increased; a 3 ns MAC now became possible.