A digital signal processor, or DSP, is a microprocessor whose architecture is tuned for the arithmetic of processing signals: audio, video, radio, sensor data, and anything else that arrives as a stream of samples. The defining computation in this domain is the multiply-accumulate, or MAC: multiply two numbers and add the result to a running total, repeated across long sequences of samples. Filtering, convolution, and transforms such as the FFT are all built from huge numbers of MACs, and a DSP is organized so that this operation runs as fast as possible.
Texas Instruments’ TMS320 family is the canonical line, and its datasheets show the design choices directly. The TMS320C25 datasheet describes a fixed-point processor built on a modified Harvard architecture with separate program and data memory spaces, so the chip can fetch an instruction and a data operand in the same cycle rather than queuing them through one bus. At its core is a dedicated hardware multiplier feeding an accumulator, letting the processor perform a multiply and an accumulate together in a single instruction cycle, the operation a general CPU would take several steps to do.
Several architectural traits recur across DSPs because they all serve the same goal of sustaining MACs. Separate buses for program and data keep the multiplier supplied without stalls. Many DSPs are fixed-point, representing fractional numbers as scaled integers, because fixed-point hardware is smaller and lower-power than full floating-point and is precise enough for many signal tasks. Special addressing modes, such as circular and bit-reversed addressing, walk through sample buffers efficiently, and dedicated repeat instructions run tight inner loops with no per-iteration overhead.
This specialization is what separates a DSP from a general-purpose CPU. A CPU is built to run arbitrary control-heavy software with branches, operating systems, and varied workloads; a DSP is built to push a steady stream of samples through arithmetic at predictable, often real-time rates. Determinism matters: a DSP filtering audio or demodulating a radio signal must finish each sample’s work within a fixed time budget, so consistent throughput is prized over the speculative tricks a CPU uses to speed up average-case code.
DSPs spread into an enormous range of devices precisely because so much of the physical world reaches computers as signals. Modems, mobile phones, hard-disk read channels, hearing aids, motor controllers, audio equipment, and countless embedded systems leaned on dedicated DSPs to do their real-time math within tight power and cost limits. The TMS320 line, introduced in the 1980s, became one of the most widely deployed processor families of its kind.
Over time the boundary blurred, as general CPUs absorbed DSP-style features through SIMD vector instructions and as dedicated accelerators took on the heaviest signal workloads. But the DSP established a lasting idea: that shaping a processor’s arithmetic and memory system around one class of math, here the multiply-accumulate at the heart of signal processing, can vastly outperform a general design at that task.