Device Model

Device modeling focuses on creating accurate and efficient representations of how devices, ranging from memory arrays to smartphones, behave in real-world conditions. Current research emphasizes developing generative models for diverse device types, including those used in neuromorphic computing and federated learning, often employing techniques like autoregressive processes and low-rank approximations to improve speed and accuracy. This work is crucial for advancing on-device machine learning, enabling more efficient and privacy-preserving applications while also addressing security vulnerabilities arising from the deployment of these models on edge devices. Furthermore, accurate device models are essential for optimizing the performance of hardware and software systems.