Qualcomm devotes significant time during its new product unveilings to talk about its Hexagon NPUs. Keen observers may recall that this branding has been reused from the company’s line of digital signal processors (DSPs), and there’s a good reason for that.

“Our journey into AI processing started probably 15 or 20 years ago, wherein our first anchor point was looking at signal processing,” said Vinesh Sukumar, Qualcomm’s head of AI products. DSPs have a similar architecture compared to NPUs, but they’re much simpler, with a focus on processing audio (e.g., speech recognition) and modem signals.

As the collection of technologies we refer to as “artificial intelligence” developed, engineers began using DSPs for more types of parallel processing, like long short-term memory (LSTM). Sukumar explained that as the industry became enamored with convolutional neural networks (CNNs), the technology underlying applications like computer vision, DSPs became focused on matrix functions, which are essential to generative AI processing as well.

While there is an architectural lineage here, it’s not quite right to say NPUs are just fancy DSPs. “If you talk about DSPs in the general term of the word, yes, [an NPU] is a digital signal processor,” said MediaTek Assistant Vice President Mark Odani. “But it’s all come a long way and it’s a lot more optimized for parallelism, how the transformers work, and holding huge numbers of parameters for processing.”

Despite being so prominent in new chips, NPUs are not strictly necessary for running AI workloads on the “edge,” a term that differentiates local AI processing from cloud-based systems. CPUs are slower than NPUs but can handle some light workloads without using as much power. Meanwhile, GPUs can often chew through more data than an NPU, but they use more power to do it. And there are times you may want to do that, according to Qualcomm’s Sukumar. For example, running AI workloads while a game is running could favor the GPU.