DSPs

Digital signal processors (DSPs) are specialized microprocessors designed to efficiently perform numerical digital signal processing in real time. They optimize tasks such as filtering, spectral analysis, modulation, and data compression by delivering high throughput for multiply-accumulate operations and other arithmetic patterns common in signal processing.

Most DSPs use a Harvard or modified Harvard architecture with separate instruction and data memories, wide data paths (often 16 or 32 bits), and a hardware MAC unit. Features frequently include zero-overhead looping, circular buffering, bit-reversed addressing for fast FFTs, and dedicated instruction sets for fixed-point or floating-point math. Fixed-point DSPs are common in embedded systems for cost and power efficiency, while floating-point variants simplify development.

Applications span audio and voice processing, telecommunications, radar and sonar, imaging, and consumer electronics such as smartphones and wearables. DSPs are typically used in real-time pipelines where deterministic latency is critical; they are often integrated into system-on-chip designs or exist as standalone processors with their own peripherals. Development usually relies on C or C++ with some assembly for critical loops, and may require fixed-point scaling and careful memory management.

In comparison with general-purpose CPUs, DSPs emphasize low latency, energy efficiency, and predictable performance for streaming data. Modern systems increasingly fuse DSP cores with ARM or other host processors, FPGAs, or GPUs, and modern toolchains support SIMD/vector extensions and DSP-oriented libraries. Notable families include Texas Instruments TMS320, Analog Devices SHARC, and Qualcomm Hexagon.