In real-world field operations, aerial grasping systems face significant challenges in dynamic environments due to strong winds, shifting surfaces, and the need to handle heavy loads. Particularly when dealing with heavy objects, the powerful propellers of the drone can inadvertently blow the target object away as it approaches, making the task even more difficult. To address these challenges, we introduce SPI- BOT, a novel drone-tethered mobile gripper system designed for robust and stable autonomous target retrieval. SPIBOT operates via a tether, much like a spider, allowing the drone to maintain a safe distance from the target. To ensure both stable mobility and secure grasping capabilities, SPIBOT is equipped with six legs and sensors to estimate the robot's and mission's states. It is designed with a reduced volume and weight compared to other hexapod robots, allowing it to be easily stowed under the drone and reeled in as needed. Designed for the 2024 MBZIRC Maritime Grand Challenge, SPIBOT is built to retrieve a 1kg target object in the highly dynamic conditions of the moving deck of a ship. This system integrates a real-time action selection algorithm that dynamically adjusts the robot's actions based on proximity to the mission goal and environmental conditions, enabling rapid and robust mission execution. Experimental results across various terrains, including a pontoon on a lake, a grass field, and rubber mats on coastal sand, demonstrate SPIBOT's ability to efficiently and reliably retrieve targets. SPIBOT swiftly converges on the target and completes its mission, even when dealing with irregular initial states and noisy information introduced by the drone.

These days, it is typical for a large-scale cluster system to have different kinds of GPUs. However, HPL (High-Performance LINPACK), the de-facto standard LINPACK implementation for evaluating the performance of a cluster system, is originally designed to work only for homogeneous CPU-only systems. In this paper, we develop SnuHPL, an optimized HPL for clusters of modern heterogeneous GPUs. To optimize SnuHPL for the heterogeneous GPUs, we design a performance model, a SnuHPL simulator based on the model, and a greedy heuristic algorithm based on the simulator. The algorithm generates the best data distribution for a given cluster configuration by considering computing power, memory capacity, and network performance altogether. We also present a simple technique to increase the energy efficiency of HPL by adjusting the core clock frequency of the GPUs. The evaluation of the data distribution algorithm on small clusters of different GPU combinations shows that it outperforms well-known other data distribution strategies. We show the effectiveness of SnuHPL on a cluster of 1,760 NVIDIA A100-80GB GPUs and 440 A100-40GB GPUs. We also show the effectiveness of the proposed energy optimization technique on a cluster of 144 A100-80GB GPUs.

A viable solution to cope with the ever-increasing computation complexity of deep learning applications is to integrate many neural processing units (NPUs) in a chip where a network-on-chip (NoC) is used as the communication fabric. Since the design space of an NoC is huge, the network topology is first selected based on the communication patterns of applications with a high-level performance estimation method. After the network topology is selected, the microarchitectural design space exploration is performed with a cycle-level NoC simulator. However, the existing NoC simulator is so slow that design space exploration of the microarchitecture is usually conducted manually in a narrow space. Since a synthetic trace is used, the simulation accuracy is also limited. To overcome these weak-nesses, we present a simulation technique that is fast and accurate enough for microarchitectural design space of an NoC. In the proposed technique, we use the real communication trace from the many-NPU simulation without NoC consideration. To this end, we define the trace format that defines the interface between a many-NPU simulator and the NoC simulator. To accelerate simulation speed, we propose a parallelization technique at the cluster level in the simulation of the hierarchical NoC. The key technique is to manage the timestamps of events at the cluster boundary to do without time synchronization error. And, we adjust the abstraction level of simulation models to reduce the number of modules in the SystemC NoC simulation. With the proposed technique, we could achieve up to 40 times speed-up for 32 NPU system, compared with the FlexNoC simulator.

Kyungho Kim, Kyungjae Lee, Seung-won Hwang, Young-In Song, Seungwook Lee. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. 2021.

In this paper, we provide an overview of the reconfigurable graphics coding (RGC) framework, in MPEG, and we present the design of 3D mesh coding based on the RGC framework. In 3D graphics, there exist many overlapping object types (e.g., IndexedFaceSet in VRML, Mesh in Collada, etc.) in various formats. In order to design a standard that supports many different object types and their combinations, the MPEG RGC framework is proposed as a unified framework that covers everything, from object type definition to object compression, in a modularized approach. The RGC framework is largely based on the reconfigurable video coding (RVC) framework in MPEG. Currently, the RGC framework further extends the RVC to support a generic object type definition. In this paper, we provide a walkthrough on the use of the RGC framework with existing graphics coding standards such as MPEG 3D mesh coding.