iis.fraunhofer.de, Feb. 20, 2025 –
A problem with many AI networks is that they're good – really good – but sometimes a little big and overly precise. That means a lot of energy, memory, and computing time are spent on complex calculations, even though simpler models could solve the task at hand more efficiently. But how do you turn an overly complicated neural network into a compact artificial intelligence (AI) that also runs reliably on small devices, for example, in a headset for sleep monitoring? This is where neural architecture search comes into play.
Designing neural networks is not unlike drawing up complicated architectural plans: it requires patience. A saving in one area increases complexity in another. An improvement there leads to ineffective use of space elsewhere. Coming up with the optimum design means making a lot of manual adjustments, and the simplest solution is easily overlooked. However, there are ways to automate this process using neural architecture search, or NAS for short. This greatly simplifies and accelerates the design process, which lets companies save money and resources in development and bring products to market faster.
NAS puts the search for the perfect neural network in the hands of computers. In a defined search space, they can use their computing power and a well-configured search strategy to recognize much more quickly which variation of a neural network offers the best performance, is the most energy-efficient, or requires particularly little memory – in each case for a specific purpose, such as object, speech, or pattern recognition in a particular healthcare or industrial application.
Tailored to the hardware: AI optimization for the edge
Certain AI applications deliver their full benefits only when neural networks are processed directly within the product. That way, the data doesn't have to migrate to the cloud, but instead is processed in real time in the devices at the edge. Because the AI has to operate on these devices' chips or microcontrollers, it faces certain limitations, so the aim is to design a network that achieves the best results given the characteristics of the hardware. To do this, Michael Rothe's team in the Communication Systems division is developing new procedures and tools to make NAS particularly hardware-aware...