Why Digital Signal Processors Are Now Everywhere in Modern Devices

When ARM CPUs first gained traction—thanks to early adopters like Apple—mobile usage surged. The benefit was clear: an embedded processor could turn any device into a versatile, feature‑rich platform. Moreover, software updates could extend the hardware’s life, allowing a single chip to power multiple product generations.

(Source: CEVA)

While general-purpose CPUs excel at a wide range of tasks, they struggle with operations that demand real‑time, low‑power processing—such as a smartphone’s modem. The modem must handle continuous analog radio signals that have been digitized, requiring rapid, efficient computation that a typical CPU cannot deliver without excessive power draw.

DSPs are engineered for such tasks. Equipped with native floating‑point support and optimized math units—especially multiply‑accumulate (MAC) operations—they process streaming data efficiently, unlike the batch‑oriented workloads typical of CPUs.

Audio processing shares these same requirements. DSPs power high‑end audio features—from equalization and Dolby compression to advanced noise‑cancelling headphones that let you sleep undisturbed on flights.

AI has expanded beyond data centers to mobile and edge devices. Today, vehicles detect pedestrians, collisions, and lane markings for basic autonomous driving. Voice‑controlled TVs, smart speakers, and even helmet‑mounted GoPros allow users to perform actions hands‑free.

These features rely on real‑time processing of streaming data—whether voice, still images, or video. Audio processing, for instance, requires high‑quality capture via multi‑mic beamforming, echo cancellation, and noise suppression—areas where DSPs have decades of proven expertise.

Command recognition employs trained neural networks—algorithms that differ markedly from CPU‑centric code. While CPUs can run them, the resulting sluggishness drains battery life. Running the network on a highly parallel architecture, such as a DSP, enables simultaneous operations and preserves power.

Some may suspect DSPs are too specialized for general developers. In reality, programming remains similar to CPUs: you write C code for both. The key is to craft code that exploits the DSP’s strengths for maximum performance.

(Source: CEVA)

DSPs are ubiquitous: every phone radio—Bluetooth, Wi‑Fi, and cellular—relies on one or more DSPs. Bluetooth earbuds, smart speakers, voice‑controlled remotes, home security cameras, and automotive sensors all employ DSPs to process audio, detect motion, and analyze sound for real‑time decision making.

GPUs, though powerful for AI training in data centers, are ill‑suited for edge use due to size, power consumption, and cost. As privacy and security demands grow, AI must shift to low‑cost, low‑power hardware—making DSPs the practical choice for embedded devices.

Consequently, embedded DSPs are proliferating. They enable low‑cost, low‑power voice control, object detection, audio enhancement, and more—all while remaining fully programmable. While CPUs still handle general management, DSPs are increasingly dominating smart audio and visual processing.

This article opens a series exploring DSPs versus hardware accelerators. Follow up posts include “When a DSP Beats a Hardware Accelerator” and “Decisions, Decisions: Hardware Accelerator or DSP?”