Under CopyrightMüller, RolandRolandMüllerMateu, LoretoLoretoMateuBrederlow, RalfRalfBrederlow2024-02-022024-02-022023-12-11https://publica.fraunhofer.de/handle/publica/459609https://doi.org/10.24406/h-45960910.1109/DCIS58620.2023.1033597910.24406/h-459609Analog and mixed-signal neural network accelerators are a promising solution to apply deep learning methods to edge applications where high energy and area efficiency are required. Such in-memory computing implementations use regular and repetitive circuit structures that take great advantage of design automation. An analog/mixed-signal standard cell design approach in combination with an automation framework has been developed to ease the design of such systems. The framework discussed here provides the basic functionality such as schematic and layout creation. It is based on manually designed standard cells and technology and topology parameters to steer the automation. The presented methodology drastically reduces the (re-)design time and engineering effort leading to a reduced time-to-market whilst errors occurring in manual executed circuit design can be avoided.enElectronic Design AutomationAnalog/Mixed-Signal CircuitsIntegrated CircuitsNeuromorphic ComputingNeuromorphic HardwareAI AcceleratorsAnalog ComputingDDC::600 Technik, Medizin, angewandte Wissenschaften::620 Ingenieurwissenschaften::620 Ingenieurwissenschaften und zugeordnete Tätigkeitenconference paper