What Is an AI Accelerator?

An AI accelerator is a processor, or part of one, built to run the maths inside neural networks. Neural networks are mostly multiply and add operations on large blocks of numbers. A CPU can do this, but slowly and at high power. An accelerator does it with many parallel arithmetic engines working on low-precision numbers, and with memory arranged so the data reaches those engines without delay.

The right type depends on the model size, the number of camera streams, the power budget and the software stack the application already uses. Some accelerators train models; at the edge, almost all of them run inference, applying a trained model to live data from cameras, microphones or machines.

The term covers very different hardware, from a whole graphics card to a small block inside a processor.

For an edge computer, the choice is usually a trade between flexibility and efficiency. The figures below are typical of the hardware in our range, not limits of each type.

Matching the accelerator to the workload keeps power, heat and cost in check. Our NPU guide explains on-chip AI, and our comparison of GPU vs NPU for edge AI covers when each one fits.

The headline figure is TOPS, trillions of operations per second, usually quoted at INT8 precision. It is a theoretical peak, and vendors may quote it at INT4 or with sparsity, which raises the number without changing the hardware. Our explainer on what TOPS means covers the details.

More useful in practice are TOPS per watt, which decides how much AI a sealed or battery-powered system can run, and frames per second on your own model. Memory matters too: a dedicated accelerator with no memory of its own must keep the model on the chip, which suits vision models but not large language models.

Much of an accelerator’s advantage comes from working with smaller numbers. Models are trained in 32-bit floating point, but inference rarely needs that accuracy, so the model is converted to 8-bit integers, or even 4-bit, before deployment. Smaller numbers need less silicon per multiplier, less memory and less data movement, so the same chip area and power budget complete several times more operations.

The conversion, called quantization, usually costs a small and measurable loss of accuracy, which is checked on real data before the model goes into service. It is also why an accelerator’s software toolchain matters as much as its hardware: the compiler decides how well the model fits the chip.

Start from the model and the data, not the TOPS figure. The questions below usually settle it.

Our Hailo AI accelerator computers use M.2 modules, and our edge AI computers cover every option. We fit and validate the accelerator, drivers and runtime in each system as standard before it ships.