ACM Reference Format: Samarjit Chakraborty1 Jingtong Hu2 Qi Zhu3. 2025. Tutorial: Design Automation for ML-enabled Cyber-Physical Systems: From Verification to Synthesis. In International Conference on Embedded Software (EMSOFT ‘25), September 28-October 3, 2025, Taipei, Taiwan. ACM, New York, NY, USA, 2 pages. https://doi.org/10.1145/3742874.3758336
Tiny battery-free devices running deep neural networks (DNNs) embody intermittent TinyML, a paradigm at the intersection of intermittent computing and deep learning, bringing sustainable intelligence to the extreme edge. This paper, as an overview of a special session at Embedded Systems Week (ESWEEK) 2025, presents four tales from diverse research backgrounds, sharing experiences in addressing unique challenges of efficient and reliable DNN inference despite the intermittent nature of ambient power. The first explores enhancing inference engines for efficient progress accumulation in hardware-accelerated intermittent inference and designing networks tailored for such execution. The second investigates computationally light, adaptive algorithms for faster, energy-efficient inference, and emerging computing-in-memory architectures for power failure resiliency. The third addresses battery-free networking, focusing on timely neighbor discovery and maintaining synchronization despite spatio-temporal energy dynamics across nodes. The fourth leverages modern nonvolatile memory fault behavior and DNN robustness to save energy without significant accuracy loss, with applicability to intermittent inference on nano-satellites. Collectively, these early efforts advance intermittent TinyML research and promote future cross-domain collaboration to tackle open challenges.
On-device AI enables real-time, privacy-preserving inference but remains constrained by the limited resources of edge devices. To address these constraints, static optimization techniques are employed to reduce model complexity, yet they often fail to handle dynamic challenges such as bursty input streams. We propose pipelined DNN inference, where a model is partitioned into sub-models that execute concurrently across heterogeneous accelerators. We examine the supporting software stack, introduce a model slicer that ensures correct partitioning, and present a multithreaded inference architecture that overlaps pipeline stages to maintain stable throughput even under surges in input rate.
Ensuring predictable and deterministic behavior in distributed cyber-physical systems (CPS) is essential for guaranteeing safety, reliability, and real-time behavior. However, achieving this predictability is challenging due to network uncertainties, asynchronous execution, and complex timing interactions. This manuscript is based on a special session at Embedded Systems Week (ESWeek) 2025, which brings together experts to explore in four presentations how this uncertainty can be addressed and how to introduce additional determinism into the system to achieve predictable timing behavior in distributed CPS. We begin by exploring cornerstones of timing analysis techniques to provide end-to-end latency guarantees for distributed systems (Chen and Gunzel). Next, we discuss design strategies for meeting timing constraints, focusing on how system parameters influence cause-effect chains and how these parameters can be tuned to ensure predictable behavior in industrial automation settings (Dasari and Becker). We then turn to approaches to achieve more predictable system behavior. To that end, we examine deterministic semantic models for distributed systems that enable the design of robust and fault-tolerant systems (Lee). Finally, we discuss how solving constraints for scheduling cause-effect chains can be used to enforce strict timing guarantees and improve predictability (Bourke).
Federated learning enables decentralized training without sharing raw data, making it suitable for privacy aware applications. However, its performance often degrades in real settings due to unknown and diverse data differences among clients. While many mitigation strategies have been proposed, they typically assume prior knowledge of the imbalance type, such as feature variation, label bias, or data quantity differences, which is unrealistic in practice. This paper addresses the overlooked problem of identifying the main type of data imbalance. We propose a method called Machine Learning based Non IID Estimator, which classifies the imbalance type by analyzing trained client models without accessing any raw data. Similarity matrices computed from model parameters are used to train standard classifiers. Evaluations on the MNIST dataset under controlled imbalance settings show that the proposed method achieves perfect classification accuracy with lightweight models. This highlights the potential of distribution type estimation as a key step toward more robust and efficient federated systems.
