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Optimizing HBM-PIM Throughput through DRAM ACT and PRE Hiding
http://doi.org/10.5626/JOK.2025.52.7.557
Modern applications, such as Large Language Models (LLMs), increasingly demand high memory bandwidth, which is difficult to meet using conventional memory devices alone. The insufficiency in memory bandwidth causes significant performance bottlenecks, as excessive time is spent on data transfers between the host processor and memory. Processing in memory (PIM) architectures address this challenge by placing processing units (PUs) near memory banks, offloading tasks from the host and leveraging DRAM internal bandwidth. HBM-PIM is one of the PIM devices developed in practice, which features one PU per two banks and enables parallel operations across all PUs. In this paper, we conduct an in-depth analysis of HBM-PIM operations, taking into account the DRAM microarchitecture. A detailed examination of HBM-PIM's microarchitecture reveals that its characteristics are not fully exploited. Based on this insight, we propose optimization techniques that leverage the structural features of HBM-PIM and can be implemented without hardware modification. By modifying the order of instructions, adjusting data mapping, and loosening memory barriers, we minimize latency caused by DRAM row conflicts and improve the performance of HBM-PIM. Our optimizations yield average performance improvements of 1.15×, 1.43×, and 1.29× for GEMV, ADD/MUL, and ReLU operations, respectively.
A Reinforcement Learning-Based Path Optimization for Autonomous Underwater Vehicle Mission Execution in Dynamic Marine Environments
Hyojun Ahn, Shincheon Ahn, Emily Jimin Roh, Ilseok Song, Jooeun Kwon, Sei Kwon, Youngdae Kim, Soohyun Park, Joongheon Kim
http://doi.org/10.5626/JOK.2025.52.6.519
This paper proposes an AOPF (Autonomous Underwater Vehicle Optimal Path Finder) algorithm for AUV mission execution and path optimization in dynamic marine environments. The proposed algorithm utilizes a PPO (Proximal Policy Optimization)-based reinforcement learning method in combination with a 3-degree-of-freedom (DOF) model, enabling a balanced approach between obstacle avoidance and effective target approach. This method is designed to achieve faster convergence and higher mission performance compared to the DDPG (Deep Deterministic Policy Gradient) algorithm. Experimental results demonstrated that the algorithm enabled stable learning and generated efficient paths. Furthermore, the proposed approach shows strong potential for real-world deployment in complex marine environments. It offers scalability to multi-AUV cooperative control scenarios.
Optimizing Throughput Prediction Models Based on Feature Category Contribution in 4G/5G Network Environments
http://doi.org/10.5626/JOK.2024.51.11.961
The acceleration in 5G technology adoption due to increased network data consumption and limitations of 4G has led to the establishment of a heterogeneous network environment comprising both 4G and limited 5G. Consequently, this highlights the importance of throughput prediction for network service quality (QoS) and resource optimization. Traditional throughput prediction research mainly relies on the use of single attributes or extraction of attributes through correlation analysis. However, these approaches have limitations, including potential exclusion of variables with nonlinear relationships with arbitrariness and inconsistency of correlation coefficient thresholds. To overcome these limitations, this paper proposed a new approach based on Feature Importance. This method could calculate the relative importance of features used in the network and assign contribution scores to attribute categories. By utilizing these scores, throughput prediction was enhanced. This approach was applied and tested on four open network datasets. Experiments demonstrated that the proposed method successfully derived an optimal category combination for throughput prediction, reduced model complexity, and improved prediction accuracy compared to using all categories.
An Automated Interior Design Model using Interior Design Guidelines and Proximal Policy Optimization
Chanyoung Yoon, Soobin Yim, Sangbong Yoo, Yun Jang
http://doi.org/10.5626/JOK.2024.51.6.519
The interior design of a residential space greatly influences the satisfaction and impression of its residents. However, interior design is not easily accessible due to its requirement for professional design knowledge. Therefore, optimization and deep learning methods for automated interior design have been proposed. Nevertheless, these technologies have encountered difficulties such as taking a considerable amount of time to solve problems or requiring extensive training data. In this paper, we propose an automated interior design model using deep reinforcement learning. In reinforcement learning, there is no need to obtain training data because the agent learns a policy that interacts with the environment and maximizes the cumulative reward. We designed interior design guidelines proposed in previous studies as a reward function to create interior layouts that satisfy functional and visual criteria. Reinforcement learning agents used PPO to arrange furniture in continuous positions. We evaluated the performance of the proposed model through two experiments: a reward comparison experiment based on different combinations of furniture and room shapes, and a design comparison experiment based on different combinations of reward functions.
Improving Portfolio Optimization Performance based on Reinforcement Learning through Episode Randomization and Action Noise
http://doi.org/10.5626/JOK.2024.51.4.370
Portfolio optimization is essential to reduce investment management risk and maximize returns. With the rapid development of artificial intelligence technology in recent years, research is being conducted to utilize it in various fields, and in particular, investigation on the application of reinforcement learning in the financial sector. However, most studies do not address the problem of agent overfitting due to iterative training on historical financial data. In this study, we propose a technique to mitigate overfitting through episode randomization and action noise in reinforcement learning-based portfolio optimization. The proposed technique randomizes the duration of the training data in each episode to experience different market conditions, thus promoting the effectiveness of data augmentation and exploration by leveraging action noise techniques to allow the agent to respond to specific situations. Experimental results show that the proposed technique improves the performance of the existing reinforcement learning agent, and comparative experiments confirm that both techniques contribute to performance improvement under various conditions.
Storage Trie Optimization Based on Ethereum Transaction Data
http://doi.org/10.5626/JOK.2024.51.2.110
Interest in blockchain has grown with the increased usage of Ethereum, thus the blockchain state data has exploded, making it difficult for users to participate in the network. In this paper, we propose a method of optimizing the storage trie, which accounts for a significant portion of state data, based on pas transaction data of real Ethereum. By deleting storage trie that never appeared during 1 million blocks from a massive 14 million block storage tire, we reduced the storage space by 19.6%, which is 10.8GB. Based on the research results of this paper, it is expected that we can propose a more effective storage trie optimization based on data.
Performance Analysis of Instruction Priority Functions using a List Scheduling Simulator
Changhoon Chung, Soo-Mook Moon
http://doi.org/10.5626/JOK.2023.50.12.1048
Instruction scheduling is an important compiler optimization technique, for reducing the execution time of a program by parallel processing. However, existing scheduling techniques show limited performance, because they rely on heuristics. This study examines the effect of instruction priority functions on list scheduling, through simulation. As a result, using a priority function based on the overall structure of the dependency graph can reduce schedule length by up to 4%, compared to using a priority function based on the original instruction order. Furthermore, the result gives a direction on which input features should be used when implementing a reinforcement learning-based scheduling model.
Improving Counterexample-Guided Bidirectional Inductive Synthesis by an Incremental Approach
Yongho Yoon, Woosuk Lee, Kwangkeun Yi
http://doi.org/10.5626/JOK.2023.50.12.1091
One of the sources of inefficiency in counterexample-guided inductive synthesis algorithms is the fresh restart of inductive synthesis for each iteration. In this paper, we propose an incremental approach for the generalized counterexample-guided bidirectional inductive synthesis algorithm. The incremental algorithm reuses knowledge from the last iteration therefore reducing the search space, and making the remaining search faster. We applied our approach to the state-of-the-art bidirectional inductive synthesis algorithm, Simba, which is based on iterative forward-backward abstract interpretation. We implemented our approach and evaluated it on a set of benchmarks from the Simba paper. The experimental results showed that, on average, our approach reduces synthesis time to 74.2% of the original, without any loss in the quality.
ESP: Improving Performance and Lifetime of High-Capacity 3D Flash Storage Using an Erase-Free Subpage Programming Technique
http://doi.org/10.5626/JOK.2023.50.1.1
Recent high-capacity 3D NAND flash devices have large page sizes. Although large pages are useful in increasing flash capacity, they can degrade both the performance and lifetime of flash storage systems when small writes are dominant. We propose a new NAND programming scheme, called erase-free sub-page programming (ESP), which allows the same page to be programmed multiple times for small writes without the intervention of the erase operation. By avoiding internal fragmentation, the ESP scheme reduces the overhead of garbage collection for large-page NAND storage. Based on the proposed ESP scheme with an adaptive retention management technique, we implemented an ESP-aware FTL(subFTL) and performed comprehensive evaluations using various benchmarks and workloads. The experimental results showed that an ESP-aware FTL could improve the IOPS and lifetime by up to 74% and 177%, respectively.
Design and Implemention of Time-Triggered Architecture for Multicore Automotive Systems
http://doi.org/10.5626/JOK.2022.49.12.1043
Recently, automotive electrical/electronic (E/E) architectures have considered the Multicore AUTOSAR platform for guaranteeing the safety and performance of automotive systems. However, inter-core communication response time delays due to spinning caused by spinlock deteriorate Multicore performance. This paper presents the design of a Time-Triggered Architecture (TTA) to optimize the Multicore system. In our approach, we present the TTA design methodology, including the task allocation algorithm using DQN reinforcement for inter-core load balancing, the Harmonic-Period setting algorithm, and the task Offset, Deadline setting algorithm. Then, we proposed a Timing Violation detection method using Data Version to apply it to the AUTOSAR platform. For verification, we applied the TTA algorithm to the Fuel Cell Controller (FCU) task model. Our simulations showed that the load balancing rate was improved by 94% compared to the existing controller, and its scalability covered at least 78% of the optimal value. It also showed that mutual exclusion was enforced and confirmed that each algorithm was well applied.
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