Fluid antenna (FA) technology has recently emerged as an effective means of exploiting spatial diversity through position-domain reconfigurability. This paper investigates the integration of FA into over-the-air federated learning (OTA-FL) systems with the aim of improving aggregation reliability and user participation under realistic channel conditions. By dynamically selecting antenna positions, FA-equipped users can exploit additional spatial degrees of freedom to realize more favorable channel conditions, thereby increasing the probability of successful contribution to the OTA aggregation process in each communication round. We consider an uplink OTA-FL framework consisting of a single fixed-antenna access point and multiple FAenabled users operating over spatially correlated fading channels. Unlike existing studies that primarily rely on optimization-based designs or numerical evaluations, we develop a tractable analytical framework that enables a rigorous performance characterization of FA-assisted OTA-FL. In particular, closed-form expressions are derived for the aggregation error outage probability and the expected number of participating users per round. Spatial channel correlation across FA ports is modeled using a copula-based approach, where the Clayton copula is adopted to capture lower-tail dependence relevant to worst-case fading conditions. Numerical results validate the analytical findings and demonstrate that FA-assisted OTA-FL significantly outperforms conventional fixed-antenna schemes in terms of aggregation reliability and participation efficiency, while providing insights under practical system considerations.

Recent advancements in machine learning offer the potential for finding faster and robust optimization approaches for analog circuit design automation. However, fully automated yet fast and PVT-robust sizing algorithms are still lacking as even the most recent methods continue to require extensive simulations or domain-specific circuit expertise. In this paper, we present a PVT-robust analog circuit sizing method, called AnaCraft, that is the first to introduce an adversarial training scheme of multi-agent reinforcement learning (RL) for robust circuit design automation. We adopt the soft actor-critic (SAC) agent for circuit sizing, which outperforms other actor-critic agents in stability and robustness. Then, we introduce a duel-play scheme to address PVT-robustness, where sizing agents cooperate to find optimal circuit parameters while competing with an adversarial PVT agent. We combine this approach with the model-based policy optimization method: an ensemble of probabilistic models is trained and used to extract many short rollouts of generated data for updating the sizing agents. We test our algorithm on the sizing of operational amplifiers in a 45nm CMOS technology, as well as on a complex data receiver circuit in a predictive 7nm FinFET technology. This demonstrates our approach’s ability to find PVT-robust power-area-optimal sizes for advanced technologies and circuits. Our proposed method achieves a higher figure of merit with up to 3x fewer circuit simulations and 2x less runtime compared to existing state-of-the-art methods.

Analog/mixed-signal circuits are key for interfacing electronics with the physical world. Their design, however, remains a largely handcrafted process, resulting in long and error-prone design cycles. While the recent rise of AI-based reinforcement learning and generative AI has created new techniques to automate this task, the need for many time-consuming simulations is a critical bottleneck hindering the overall efficiency. Furthermore, the lack of explainability of the resulting design solutions hampers widespread adoption of the tools. To address these issues, a novel agentic AI framework for sample-efficient and explainable analog circuit sizing is presented. It employs a multi-agent workflow where specialized Large Language Model (LLM)-based agents collaborate to interpret the circuit topology, to understand the design goals, and to iteratively refine the circuit’s design parameters towards the target goals with human-interpretable reasoning. The adaptive simulation strategy creates an intelligent control that yields a high sample efficiency. The AnaFlow framework is demonstrated for two circuits of varying complexity and is able to complete the sizing task fully automatically, differently from pure Bayesian optimization and reinforcement learning approaches. The system learns from its optimization history to avoid past mistakes and to accelerate convergence. The inherent explainability makes this a powerful tool for analog design space exploration and a new paradigm in analog EDA, where AI agents serve as transparent design assistants.

This paper investigates an over-the-air federated learning (OTA-FL) system that employs fluid antennas (FAs) at an access point. The system enhances learning performance by leveraging the additional degrees of freedom provided by antenna mobility. We analyze the convergence of the OTA-FL system and derive the optimality gap to illustrate the influence of FAs on learning performance. With these results, we formulate a nonconvex optimization problem to minimize the optimality gap by jointly optimizing the positions of the FAs, the beamforming vector, and the transmit power allocation at each user. To address the dynamic environment, we cast this optimization problem as a Markov decision process and propose the recurrent deterministic policy gradient (RDPG) algorithm. Finally, extensive simulations show that the FA-assisted OTA-FL system outperforms systems with fixed-position antennas and that the RDPG algorithm surpasses the existing methods.

This paper proposes an innovative approach to improve over-the-air federated learning (OTA-FL) systems by integrating fluid antennas (FAs) at the access point. By exploiting the mobility of FAs, we aim to increase the correlation among the users’ channels, thereby improving the learning performance. We analyze the performance of over-the-air computation and the convergence behavior of the OTA-FL system, highlighting the benefits of FAs. Since the learning performance improves as more devices participate in the FL aggregation, we formulate a non-convex optimization problem that maximizes the number of selected users by jointly optimizing FA positions and the beamforming vector, coupled with a user selection policy subject to a mean-squared error constraint. To address environmental dynamics, we describe the problem as a Markov decision process and develop a long short-term memory (LSTM)-based algorithm for efficient decision-making. Simulation results demonstrate that the proposed FA-assisted OTA-FL framework significantly outperforms conventional setups, achieving higher user selection rates and improved learning performance compared to existing benchmarks.

Despite recent advances in algorithms, such as the use of reinforcement learning, analog circuit sizing optimization remains a challenging task that demands numerous circuit simulations, hence extensive CPU times. This paper introduces the application of Model-Based Policy Optimization (MBPO) to highly boost the sample efficiency of reinforcement learning for analog circuit sizing. This method leverages an ensemble of probabilistic dynamic models to generate short rollouts branched from real data for a fast but extensive exploration of the design space, thereby speeding up the learning process of the reinforcement learning agent and improving its convergence. Integrated in the Twin Delayed DDPG (TD3) algorithm, our new model-based TD3 (MBTD3) approach is validated on analog circuits of different complexity, outperforming the existing model-free TD3 method by achieving power/area-optimal design solutions within up to ~3x fewer simulations and half the run time. In addition, for larger analog circuits, we present a multi-agent version of MBTD3, in which multiple simultaneous agents use global probabilistic models for sizing the different sub-blocks within the circuit. Demonstrated for a complex data receiver circuit, it surpasses the model-free multi-agent TD3 method with ~2x less simulations and half the run time. The proposed novel algorithms clearly boost the efficiency of automated analog circuit sizing.

In this work, to limit the number of required attention inference hops in memory-augmented neural networks, we propose an online adaptive approach called [Formula: see text]-memory-augmented neural network (MANN). By exploiting a small neural network classifier, an adequate number of attention inference hops for the input query are determined. The technique results in the elimination of a large number of unnecessary computations in extracting the correct answer. In addition, to further lower computations in [Formula: see text]-MANN, we suggest pruning weights of the final fully connected (FC) layers. To this end, two pruning approaches, one with negligible accuracy loss and the other with controllable loss on the final accuracy, are developed. The efficacy of the technique is assessed by applying it to two different MANN structures and two question answering (QA) datasets. The analytical assessment reveals, for the two benchmarks, on average, 50% fewer computations compared to the corresponding baseline MANNs at the cost of less than 1% accuracy loss. In addition, when used along with the previously published zero-skipping technique, a computation count reduction of approximately 70% is achieved. Finally, when the proposed approach (without zero skipping) is implemented on the CPU and GPU platforms, on average, a runtime reduction of 43% is achieved.