This chapter provides an essential guide for designers to understand the Digital signal processors (DSPs) and briefly compares microprocessors and DSPs. Digital signal processors have traditionally been optimized to compute finite impulse response convolutions (sum of products), infinite impulse response recursive filtering, and FFT-type (butterfly) operations that typically characterize most signal-processing algorithms. One of the most common signal-processing functions is linear filtering. High-pass, low-pass, and band-pass filters, which traditionally are analog designs, can be constructed with DSP techniques. The simplest processor memory structure is a single bank of memory, which the processor accesses through a single set of address and data lines. This structure, which is common among non-DSP processors, is often considered a von Neumann architecture. Generally, a DSP instruction set is tailored to the computation-intensive algorithms common to DSP applications. This is possible because the instruction set allows data movement between various computational units with minimum overhead. For example, sustained single-cycle multiplication/ accumulation operations are possible.