Intelligent agents in real-world applications must adapt their behavior to changing contexts and user preferences. For example, planning a road trip requires considering both travel time and cost. Multi-objective reinforcement learning (MORL) provides a principled approach to navigate such trade-offs. However, most existing approaches require predefined preference weights during training and jointly optimize the model for all objectives. In this paper, we introduce TORA (Train Once, Realign Anytime), a novel framework that defers preference integration to inference time, enabling flexible adaptation to user preferences without retraining. TORA independently trains diffusion planning models for each objective and combines them at inference time using user-specified preferences to generate behavior aligned with desired trade-offs. Furthermore, new objectives can be added seamlessly by training additional models without modifying existing ones. Empirical evaluations on standard offline MORL benchmarks demonstrate that TORA achieves competitive and consistent performance compared to methods that require fixed preference weights.

The Gaussian image filter is a compute-intensive approach to reduce undesirable artifacts and generally serves as a pre-processing technique for emerging applications related to visual computing systems. This work evaluates alternatives for the design of power-efficient Gaussian Filters. The proposed optimization strategy combines: 1) a refactored function to minimize the arithmetic operations, and 2) a design space exploration investigating different approximation scenarios applied to the full adders. The exact version of our refactored Gaussian Filter architecture reduces the total power consumption and the circuit area by 18% and 12%, respectively compared with the baseline Gaussian Filter architecture. Moreover, the combination of different approximation levels with the refactored architecture provides design options with power reductions from 21% to 59% compared with the baseline Gaussian Filter architecture.

The recently finalized Versatile Video Coding (VVC) standard brings a number of new tools to substantially improve coding efficiency, which resulted in a significant increase in complexity. Therefore, the use of such new standard in mobile devices requires not only the use of dedicated VLSI hardware architectures, but also the adoption of techniques that could reduce its complexity to meet the real-time and energy efficiency requirements. In this context, the most intensive encoding tools, such as the Fractional Motion Estimation (FME), are the first ones to be considered for optimization. Thereby, this work presents three different search patterns for the FME that are able to reduce the complexity of VVC by creating fixed search windows around the most relevant candidates. Our experiments show that these patterns result in acceptable decreases in coding efficiency, with average BD-Rates in the range of 0.56% - 0.33%, for the LD-P configuration, and 0.47% - 0.21% for the RA configuration, while allowing for significant hardware optimization. Comparing to a state-of-the-art FME hardware architecture that searches over 48 candidates the three proposed patterns can operate in higher frequencies to achieve the same throughput and require less area, leading to less power consumption and higher energy efficiency. Particularly, one of the three patterns may lead to a 60% area reduction and 53% less dynamic power consumption.

The proper management of the signal to noise ratio margin is important for improving the stability of digital subscriber line (DSL) networks and increasing the customer satisfaction regarding services such as triple-play. This work presents a novel algorithm for multiuser margin optimization that benefits from the relationships between power and margin adaptation. The proposed multiuser margin maximization (MMM) is a meta algorithm that repeatedly invokes a spectrum balancing base algorithm. The base algorithm corresponds to a dynamic spectrum management (DSM) algorithm of level one or two, and is responsible for part of the overall spectra optimization procedure. Simulation results illustrate the performance of the proposed algorithm when using two base algorithms: iterative water-filling (IWF) and iterative spectrum balancing (ISB). The results show that the proposed MMM is able to design better noise margins and spectra, providing performance improvements when compared to a previous margin optimization algorithm that is restricted to using DSM level 1 coordination.

Spectrum balancing (SB) techniques optimize transmission and can significantly improve digital subscriber lines (DSL) services. In the literature, the DSL system optimization is typically formulated as a rate maximization problem. However, there is an increasing interest in minimizing the considerable amount of power consumed by telecommunication networks. Few works in the SB literature have explored algorithms for power minimization. It is known that some existing solutions for rate maximization can be converted into power minimization algorithms. This relation has not been fully explored and, consequently, the area lacks results regarding what can be achieved with power minimization SB algorithms. This work aims to diminish this gap. First, the equivalence between rate maximization and power minimization problems is formalized. Second, extended versions of some rate maximization SB algorithms are proposed for power minimization purposes and evaluated through simulations. In addition, the power-usage capabilities and convergence characteristics of each extended SB algorithms is discussed.