In this paper, we introduce innovative approaches for accelerating the Jacobi method for matrix diagonalization, specifically through the formulation of large matrix diagonalization as a Semi-Markov Decision Process and small matrix diagonalization as a Markov Decision Process. Furthermore, we examine the potential of utilizing scalable architecture between different-sized matrices. During a short training period, our method discovered a significant reduction in the number of steps required for diagonalization and exhibited efficient inference capabilities. Importantly, this approach demonstrated possible scalability to large-sized matrices, indicating its potential for wide-ranging applicability. Upon training completion, we obtain action-state probabilities and transition graphs, which depict transitions between different states. These outputs not only provide insights into the diagonalization process but also pave the way for cost savings pertinent to large-scale matrices. The advancements made in this research enhance the efficacy and scalability of matrix diagonalization, pushing for new possibilities for deployment in practical applications in scientific and engineering domains.

Spiking neural network (SNN) is studied in multidisciplinary domains to (i) enable order-of-magnitudes energy-efficient AI inference and (ii) computationally simulate neuro-scientific mechanisms. The lack of discrete theory obstructs the practical application of SNN by limiting its performance and nonlinearity support. We present a new optimization-theoretic perspective of the discrete dynamics of spiking neurons. We prove that a discrete dynamical system of simple integrate-and-fire models approximates the sub-gradient method over unconstrained optimization problems. We practically extend our theory to introduce a novel sign gradient descent (signGD)-based neuronal dynamics that can (i) approximate diverse nonlinearities beyond ReLU and (ii) advance ANN-to-SNN conversion performance in low time steps. Experiments on large-scale datasets show that our technique achieves (i) state-of-the-art performance in ANN-to-SNN conversion and (ii) is the first to convert new DNN architectures, e.g., ConvNext, MLP-Mixer, and ResMLP. We publicly share our source code at https://github.com/snuhcs/snn_signgd .

Spiking Neural Networks (SNNs) have shown capabilities for solving diverse machine learning tasks with ultra-low-power/energy computation. To further improve the performance and efficiency of SNN inference, the Compute-in-Memory (CIM) paradigm with emerging device technologies such as resistive random access memory is employed. However, most of SNN architectures are developed without considering constraints from the application and the underlying CIM hardware (e.g., memory, area, latency, and energy consumption). Moreover, most of SNN designs are derived from the Artificial Neural Networks, whose network operations are different from SNNs. These limitations hinder SNNs from reaching their full potential in accuracy and efficiency. Toward this, we propose HASNAS, a novel hardware-aware spiking neural architecture search (NAS) framework for neuromorphic CIM systems that finds an SNN that offers high accuracy under the given memory, area, latency, and energy constraints. To achieve this, HASNAS employs the following key steps: (1) optimizing SNN operations to achieve high accuracy, (2) developing an SNN architecture that facilitates an effective learning process, and (3) devising a systematic hardware-aware search algorithm to meet the constraints. The experimental results show that our HASNAS quickly finds an SNN that maintains high accuracy compared to the state-of-the-art by up to 11x speed-up, and meets the given constraints: 4x10^6 parameters of memory, 100mm^2 of area, 400ms of latency, and 120uJ energy consumption for CIFAR10 and CIFAR100; while the state-of-the-art fails to meet the constraints. In this manner, our HASNAS can enable efficient design automation for providing high-performance and energy-efficient neuromorphic CIM systems for diverse applications.

However, the well-established methods for obtaining posterior distributions of likely parameters such as Markov chain Monte Carlo (MCMC) sampling [1] become infeasible with highly parameterized machine learning function representations, such as neural networks (NNs). The curse of dimensionality in this uncertainty quantification (UQ) setting is tied to the cost of sampling the posterior sufficiently to determine its covariance structure and generate representative push-forward realizations. In this work, our goal is to obtain a high-dimensional posterior distribution over a large number of random variables representing model parameters, which is particularly useful when limited amount of training data is available. One of the simplest ways to obtain the approximate posterior is to implement MCMC methods. However, this approach is challenged by the number of parameters typically present in NNs and it is difficult to converge samples to those representative of the posterior, even for models with moderate dimensionality. There has been enormous progress made to approximate high-dimensional posterior distributions using variational inference methods [2]. However, these methods restrict the approximate posterior to a certain parametric family and find the best approximate posterior through optimization. To surmount these issues, Liu and Wang [3] recently proposed a non-parametric variational inference method called Stein variational gradient descent (SVGD).

Driven by advancements in sensing and computing, deep reinforcement learning (DRL)-based methods have demonstrated significant potential in effectively tackling distribution system restoration (DSR) challenges under uncertain operational scenarios. However, the data-intensive nature of DRL poses obstacles in achieving satisfactory DSR solutions for large-scale, complex distribution systems. Inspired by the transformative impact of emerging foundation models, including large language models (LLMs), across various domains, this paper explores an innovative approach harnessing LLMs' powerful computing capabilities to address scalability challenges inherent in conventional DRL methods for solving DSR. To our knowledge, this study represents the first exploration of foundation models, including LLMs, in revolutionizing conventional DRL applications in power system operations. Our contributions are twofold: 1) introducing a novel LLM-powered Physics-Informed Decision Transformer (PIDT) framework that leverages LLMs to transform conventional DRL methods for DSR operations, and 2) conducting comparative studies to assess the performance of the proposed LLM-powered PIDT framework at its initial development stage for solving DSR problems. While our primary focus in this paper is on DSR operations, the proposed PIDT framework can be generalized to optimize sequential decision-making across various power system operations.

Safety issues at construction sites have long plagued the industry, posing risks to worker safety and causing economic damage due to potential hazards. With the advancement of artificial intelligence, particularly in the field of computer vision, the automation of safety monitoring on construction sites has emerged as a solution to this longstanding issue. Despite achieving impressive performance, advanced object detection methods like YOLOv8 still face challenges in handling the complex conditions found at construction sites. To solve these problems, this study presents the Global Stability Optimization YOLO (GSO-YOLO) model to address challenges in complex construction sites. The model integrates the Global Optimization Module (GOM) and Steady Capture Module (SCM) to enhance global contextual information capture and detection stability. The innovative AIoU loss function, which combines CIoU and EIoU, improves detection accuracy and efficiency. Experiments on datasets like SODA, MOCS, and CIS show that GSO-YOLO outperforms existing methods, achieving SOTA performance.

In autonomous aerial navigation, real-time and energy-efficient obstacle avoidance remains a significant challenge, especially in dynamic and complex indoor environments. This work presents a novel integration of neuromorphic event cameras with physics-driven planning algorithms implemented on a Parrot Bebop2 quadrotor. Neuromorphic event cameras, characterized by their high dynamic range and low latency, offer significant advantages over traditional frame-based systems, particularly in poor lighting conditions or during high-speed maneuvers. We use a DVS camera with a shallow Spiking Neural Network (SNN) for event-based object detection of a moving ring in real-time in an indoor lab. Further, we enhance drone control with physics-guided empirical knowledge inside a neural network training mechanism, to predict energy-efficient flight paths to fly through the moving ring. This integration results in a real-time, low-latency navigation system capable of dynamically responding to environmental changes while minimizing energy consumption. We detail our hardware setup, control loop, and modifications necessary for real-world applications, including the challenges of sensor integration without burdening the flight capabilities. Experimental results demonstrate the effectiveness of our approach in achieving robust, collision-free, and energy-efficient flight paths, showcasing the potential of neuromorphic vision and physics-driven planning in enhancing autonomous navigation systems.

The recent emergence of Large Language Models (LLMs) has reshaped the landscape of natural language processing, ushering in a new era of computational linguistics. Bolstered by advancements in machine learning and deep neural networks, LLMs have garnered widespread attention for their remarkable ability to understand and generate human-like text. This transformative technology has revolutionized various applications, ranging from machine translation and sentiment analysis to chatbots and content generation. By leveraging vast amounts of text data and sophisticated algorithms, LLMs have demonstrated unparalleled proficiency in capturing linguistic nuances, contextual dependencies, and semantic meanings.

Task and Motion Planning (TAMP) integrates high-level task planning and low-level motion planning to equip robots with the autonomy to effectively reason over long-horizon, dynamic tasks. Optimization-based TAMP focuses on hybrid optimization approaches that define goal conditions via objective functions and are capable of handling open-ended goals, robotic dynamics, and physical interaction between the robot and the environment. Therefore, optimization-based TAMP is particularly suited to solve highly complex, contact-rich locomotion and manipulation problems. This survey provides a comprehensive review on optimization-based TAMP, covering (i) planning domain representations, including action description languages and temporal logic, (ii) individual solution strategies for components of TAMP, including AI planning and trajectory optimization (TO), and (iii) the dynamic interplay between logic-based task planning and model-based TO. A particular focus of this survey is to highlight the algorithm structures to efficiently solve TAMP, especially hierarchical and distributed approaches. Additionally, the survey emphasizes the synergy between the classical methods and contemporary learning-based innovations such as large language models. Furthermore, the future research directions for TAMP is discussed in this survey, highlighting both algorithmic and application-specific challenges.

If you want evidence of Microsoft's progress towards its environmental "moonshot" goal, then look closer to earth: at a building site on a west London industrial estate. The company's Park Royal datacentre is part of its commitment to drive the expansion of artificial intelligence (AI), but that ambition is jarring with its target of being carbon negative by 2030. Microsoft says the centre will be run fully on renewable energy. However, the construction of datacentres and the servers they are filled with means that the company's scope 3 emissions – such as CO2 related to the materials in its buildings and the electricity people consume when using products such as Xbox – are more than 30% above their 2020 level. As a result, the company is exceeding its overall emissions target by roughly the same rate.