Digital Signal Processor (DSP)
A Digital Signal Processor (DSP) is a processor built to handle streams of numbers quickly and efficiently: audio samples from a microphone, pixels from a camera, or readings from motion sensors. In a phone, it is the quiet, power-conscious worker that can keep listening or filtering data without waking the main CPU for every tiny task.
What makes a DSP different
A general-purpose CPU is designed to run many kinds of software, while a DSP is tuned for the repeated math behind signal processing and parts of machine learning. It can perform operations such as multiply-and-accumulate—central to convolutions, filters, and neural-network layers—on many values in parallel. DSPs also work efficiently with fixed-point integer arithmetic, which uses less energy and memory than floating point when a model has been quantized.
Its role in an edge AI device
Mobile chips commonly pair CPUs, GPUs, NPUs, and DSPs because each is suited to a different job. A DSP is particularly useful for continuous, low-latency workloads:
- A wake-word model monitors microphone audio while the rest of the phone remains in a low-power state.
- A fitness watch filters accelerometer data and recognizes gestures.
- A camera pipeline denoises, resizes, or extracts features before a larger vision model runs on an NPU.
Qualcomm’s Hexagon DSP, for example, includes vector hardware used for audio, sensor processing, and selected AI inference workloads. Runtimes can route compatible model operations to it; TensorFlow Lite provides a Hexagon delegate for this purpose.
Why it matters
Sending every sensor sample to the CPU burns battery and can add delay. Sending it to the cloud adds network dependence and privacy exposure. A DSP lets an edge device process the always-on “front end” locally within a tight power and thermal budget. Its limits matter too: DSP memory is constrained, supported neural-network operations vary, and data transfers between processors cost time and energy. A good deployment assigns lightweight, streaming work to the DSP and reserves heavier model stages for an NPU, GPU, or CPU.
A Digital Signal Processor (DSP) is a specialized processor optimized for fast, energy-efficient numerical operations on continuous data such as audio, images, radio signals, and sensor streams. In mobile Edge AI, DSPs can execute signal preprocessing and selected neural-network operations with lower power and latency than general-purpose CPUs, enabling always-on features such as voice wake-word detection and camera enhancement.
Think of a Digital Signal Processor (DSP) as a specialist in a phone’s workshop. While the main processor handles many different jobs, the DSP is built to quickly handle streams of everyday signals: sound from a microphone, images from a camera, or movement from a sensor.
For example, it can help a phone reduce background noise during a call, listen for a wake word such as “Hey Siri,” or prepare camera data for an AI feature. Because it is designed for these repeating tasks, it can often do them using less battery power than the main processor. That makes it especially useful for always-on features that need to work directly on the device.