Mapping a dataflow - graph of an ML model onto a reconfigurable system is difficult, as different mappings have different throughputs and consume resource constraints differently. To solve this, a model to evaluate the throughput of mappings is necessary as measuring throughput completely is expensive. Many use a hand - designed analytical model, relying on proxy features or intuition, introducing error. We provide a Learned Approach that predicts throughput 31% - 52% more accurately over a variety of graphs. In addition, our approach shows no accuracy degradation after removing performance annotations. We show that using this approach results in 5.6% faster compiled graphs.

High-level synthesis (HLS) is widely used for transferring behavior-level specifications into circuit-level implementations. As a critical step in HLS, scheduling arranges the execution order of operations for enhanced performance.

Applications based on Large Language Models (LLMs) contains a series of tasks to address real-world problems with boosted capability, which have dynamic demand volumes on diverse backends. Existing serving systems treat the resource demands of LLM applications as a blackbox, compromising end-to-end efficiency due to improper queuing order and backend warm up latency. We find that the resource demands of LLM applications can be modeled in a general and accurate manner with Probabilistic Demand Graph (PDGraph). We then propose Hermes, which leverages PDGraph for efficient serving of LLM applications. Confronting probabilistic demand description, Hermes applies the Gittins policy to determine the scheduling order that can minimize the average application completion time. It also uses the PDGraph model to help prewarm cold backends at proper moments. Experiments with diverse LLM applications confirm that Hermes can effectively improve the application serving efficiency, reducing the average completion time by over 70% and the P95 completion time by over 80%.

Cooking tasks remain a challenging problem for robotics due to their complexity. Videos of people cooking are a valuable source of information for such task, but introduces a lot of variability in terms of how to translate this data to a robotic environment. This research aims to streamline this process, focusing on the task plan generation step, by using a Large Language Model (LLM)-based Task and Motion Planning (TAMP) framework to autonomously generate cooking task plans from videos with subtitles, and execute them. Conventional LLM-based task planning methods are not well-suited for interpreting the cooking video data due to uncertainty in the videos, and the risk of hallucination in its output. To address both of these problems, we explore using LLMs in combination with Functional Object-Oriented Networks (FOON), to validate the plan and provide feedback in case of failure. This combination can generate task sequences with manipulation motions that are logically correct and executable by a robot. We compare the execution of the generated plans for 5 cooking recipes from our approach against the plans generated by a few-shot LLM-only approach for a dual-arm robot setup. It could successfully execute 4 of the plans generated by our approach, whereas only 1 of the plans generated by solely using the LLM could be executed.

New materials have long marked the civilization level, serving as an impetus for technological progress and societal transformation. The classic structure-property correlations were key of materials science and engineering. However, the knowledge of materials faces significant challenges in adapting to exclusively data-driven approaches for new material discovery. This perspective introduces the concepts of functional units (FUs) to fill the gap in understanding of material structure-property correlations and knowledge inheritance as the "composition-microstructure" paradigm transitions to a data-driven AI paradigm transitions. Firstly, we provide a bird's-eye view of the research paradigm evolution from early "process-structure-properties-performance" to contemporary data-driven AI new trend. Next, we highlight recent advancements in the characterization of functional units across diverse material systems, emphasizing their critical role in multiscale material design. Finally, we discuss the integration of functional units into the new AI-driven paradigm of materials science, addressing both opportunities and challenges in computational materials innovation.

Modern robotic systems, deployed across domains from industrial automation to domestic assistance, face a critical challenge: executing tasks with precision and adaptability in dynamic, unpredictable environments. To address this, we propose STAR (Smart Task Adaptation and Recovery), a novel framework that synergizes Foundation Models (FMs) with dynamically expanding Knowledge Graphs (KGs) to enable resilient task planning and autonomous failure recovery. While FMs offer remarkable generalization and contextual reasoning, their limitations, including computational inefficiency, hallucinations, and output inconsistencies hinder reliable deployment. STAR mitigates these issues by embedding learned knowledge into structured, reusable KGs, which streamline information retrieval, reduce redundant FM computations, and provide precise, scenario-specific insights. The framework leverages FM-driven reasoning to diagnose failures, generate context-aware recovery strategies, and execute corrective actions without human intervention or system restarts. Unlike conventional approaches that rely on rigid protocols, STAR dynamically expands its KG with experiential knowledge, ensuring continuous adaptation to novel scenarios. To evaluate the effectiveness of this approach, we developed a comprehensive dataset that includes various robotic tasks and failure scenarios. Through extensive experimentation, STAR demonstrated an 86% task planning accuracy and 78% recovery success rate, showing significant improvements over baseline methods. The framework's ability to continuously learn from experience while maintaining structured knowledge representation makes it particularly suitable for long-term deployment in real-world applications.

The inherent probabilistic nature of Large Language Models (LLMs) introduces an element of unpredictability, raising concerns about potential discrepancies in their output. This paper introduces an innovative approach aims to generate correct and optimal robotic task plans for diverse real-world demands and scenarios. LLMs have been used to generate task plans, but they are unreliable and may contain wrong, questionable, or high-cost steps. The proposed approach uses LLM to generate a number of task plans as trees and amalgamates them into a graph by removing questionable paths. Then an optimal task tree can be retrieved to circumvent questionable and high-cost nodes, thereby improving planning accuracy and execution efficiency. The approach is further improved by incorporating a large knowledge network. Leveraging GPT-4 further, the high-level task plan is converted into a low-level Planning Domain Definition Language (PDDL) plan executable by a robot. Evaluation results highlight the superior accuracy and efficiency of our approach compared to previous methodologies in the field of task planning.

Robots can be very useful to automate tasks and reduce the human effort required. But for the robot to know, how to perform tasks, we need to give it a clear set of steps to follow. It is nearly impossible to provide a robot with instructions for every possible task. Therefore we have a Universal Functional object-oriented network (FOON) which was created and expanded and has a lot of existing recipe information [1]. But certain tasks are complicated for robots to perform and similarly, some tasks are complicated for humans to perform. Therefore weights have been added to functional units to represent the chance of successful execution of the motion by the robot [2]. Given a set of kitchen items and a goal node, using Universal FOON, a robot must be able to determine if the required items are present in the kitchen, and if yes, get the steps to convert the required kitchen items to the goal node. Now through this paper, we use two algorithms (IDS and GBFS) to retrieve a task tree (if possible) for a goal node and a given set of kitchen items. The following would be the different parts of the paper: Section II FOON creation, where we will discuss the different terminologies related to FOON and visualization of FOON. In Section III Methodology we discuss the IDS and GBFS search algorithms and the two different heuristics implemented and used in GBFS. In Section IV Experiment/Discussion, we compare the performance of different algorithms. In the final section V, we specify the references of the papers that have been cited.

This paper is based on developing different algorithms, which generate the task tree planning for the given goal node(recipe). The knowledge representation of the dishes is called FOON. It contains the different objects and their between them with respective to the motion node The graphical representation of FOON is made by noticing the change in the state of an object with respect to the human manipulators. We will explore how the FOON is created for different recipes by the robots. Task planning contains difficulties in exploring unknown problems, as its knowledge is limited to the FOON. To get the task tree planning for a given recipe, the robot will retrieve the information of different functional units from the knowledge retrieval process called FOON. Thus the generated subgraphs will allow the robot to cook the required dish. Thus the robot can able to cook the given recipe by following the sequence of instructions.