DIGITAL 3T-EDRAM BASED CIM MACRO FOR ACCELERATING MATRIX MULTIPLICATIONS IN TRANSFORMER MODEL WITH HIGH-ACCURACY AND HIGH COMPUTE-EFFICIENCY

트랜스포머 모델의 행렬곱 연산 가속을 위한 높은 정확도 및 연산 효율성을 갖는 디지털 기반 3T-eDRAM CIM 매크로 기술이 개시된다. 3T(three transistors) eDRAM(embedded DRAM) 셀 기반의 CIM(compute-in-memory) 매크로 구조로서 가중치 데이터 저장을 위한 메모리 어레이, 메모리 주변회로, MAC(multiply-accumulate) 연산을 위한 연산기, 및 매크로 컨트롤러를 포함하고, 상기 메모리 어레이는 동시에 읽기 동작을 수행하는 복수 개의 서브 어레이로 구성되고, 상기 연산기는 상기 메모리 어레이에서 동시에 읽어온 가중치 데이터(weight)와 상기 매크로 컨트롤러를 통해 입력된 입력 데이터(activation)의 MAC 연산을 수행하는 근접 셀 맥 트리로 구성될 수 있다

Achieving Enhanced High-Temperature Performance of Lithium-Ion Batteries via Salt-Inspired Interfacial Engineering

Electrolyte additive engineering enables the creation of long-lasting interfacial layers that protect electrodes, thus extending the lifetime of high-energy lithium-ion batteries employing Ni-rich Li[Ni1-x-yCoxMny]O2 (NCM) cathodes. However, batteries face various limitations if existing additives are employed alone without an appropriate combination. Herein, the study reports the development of a molecular-engineered salt-type multifunctional additive, lithium bis(phosphorodifluoridate) triethylammonium ethenesulfonate (LiPENS), that leverages the different functionalities of phosphorous, nitrogen, and sulfur-embedded motifs, as well as the classical additive vinylene carbonate (VC), to construct protective interfacial layers. The thermally and electrochemically reinforced solid electrolyte interphase (SEI), achieved through the combined use of LiPENS and VC, conserves the lithiation level of the Graphite (Gr) anode with minimal SEI growth, whereas the inorganic-rich cathode-electrolyte interface (CEI) alleviates the irrevocable phase transition and mechanical fragility of the LiNi0.8Co0.1Mn0.1O2 (NCM811) secondary particles. The multifunctional roles of LiPENS are demonstrated in an NCM811/Gr full cell, showing a discharge capacity of 190.7 mAh g-1 with an enhanced capacity retention of 91.8% at 1 C and 45 degrees C after 300 cycles. This advancement in electrolyte additive engineering based on salt structures can lead to more efficient, reliable, and commercially viable batteries for high-energy applications, including electric vehicles and portable electronics.

A multifrequency study of sub-parsec jets with the Event Horizon Telescope

Context. The 2017 observing campaign of the Event Horizon Telescope (EHT) delivered the first very long baseline interferometry (VLBI) images at the observing frequency of 230 GHz, leading to a number of unique studies on black holes and relativistic jets from active galactic nuclei (AGN). In total, eighteen sources were observed, including the main science targets, Sgr A* and M 87, and various calibrators. Sixteen sources were AGN. Aims. We investigated the morphology of the sixteen AGN in the EHT 2017 data set, focusing on the properties of the VLBI cores: size, flux density, and brightness temperature. We studied their dependence on the observing frequency in order to compare it with the Blandford-Konigl (BK) jet model. In particular, we aimed to study the signatures of jet acceleration and magnetic energy conversion. Methods. We modeled the source structure of seven AGN in the EHT 2017 data set using linearly polarized circular Gaussian components (1749+096, 1055+018, BL Lac, J0132-1654, J0006-0623, CTA 102, and 3C 454.3) and collected results for the other nine AGN from dedicated EHT publications, complemented by lower frequency data in the 2-86 GHz range. Combining these data into a multifrequency EHT+ data set, we studied the dependences of the VLBI core component flux density, size, and brightness temperature on the frequency measured in the AGN host frame (and hence on the distance from the central black hole), characterizing them with power law fits. We compared the observations with the BK jet model and estimated the magnetic field strength dependence on the distance from the central black hole. Results. Our observations spanning event horizon to parsec scales indicate a deviation from the standard BK model, particularly in the decrease of the brightness temperature with the observing frequency. Only some of the discrepancies may be alleviated by tweaking the model parameters or the jet collimation profile. Either bulk acceleration of the jet material, energy transfer from the magnetic field to the particles, or both are required to explain the observations. For our sample, we estimate a general radial dependence of the Doppler factor delta proportional to r(<= 0.5). This interpretation is consistent with a magnetically accelerated sub-parsec jet. We also estimate a steep decrease of the magnetic field strength with radius B proportional to r(-3), hinting at jet acceleration or efficient magnetic energy dissipation.

A Novel Efficient Crash Consistency Solution Enabling Rollback Recovery for Secure NVM in Low-Power Energy Harvesting Systems

Energy Harvesting Systems (EHSs) frequently suffer power failures and are particularly deployed in remote and open environments where physical access attacks on Non-volatile Memories (NVMs) are practical. However, prior crash consistency solutions for secure NVM were designed only for conventional power-rich systems with the assumption that enough power is steadily supplied. Moreover, the prior solutions rely on roll-forward recovery and cause a significant performance overhead in low-power EHSs. To achieve a low-cost and high-performance crash-consistent secure NVM working on low-power EHSs, this paper presents Milestone, the first efficient crash consistency solution that introduces a novel hybrid checkpoint mechanism to enable a rollback recovery for secure NVM working in frequent power failures.The hybrid checkpointing atomically (1) undo-logs data updates from program writes and (2) redo-logs the updates of security metadata associated with the data updates when an adaptive hardware timer expires. In particular, Milestone discovers an optimized eager update method for the security metadata that can be performed in parallel with the program writes to NVM by leveraging the rollback recovery. Our experimental results demonstrate that Milestone significantly outperforms the state-of-the-art roll-forward recovery-based solution for secure NVM running on low-power EHSs, achieving up to a 1.87x speedup, on average.

InstaGraM: Instance-Level Graph Modeling for Vectorized HD Map Learning

For scalable autonomous driving, a robust map-based localization system, independent of GPS, is fundamental. To achieve such map-based localization, online high-definition (HD) map construction plays a significant role in accurate estimation of the pose. Although recent advancements in online HD map construction have predominantly investigated on vectorized representation due to its effectiveness, they suffer from computational cost and fixed parametric model, which limit scalability. To alleviate these limitations, we propose a novel HD map learning framework that leverages graph modeling. This framework is designed to learn the construction of diverse geometric shapes, thereby enhancing the scalability of HD map construction. Our approach involves representing the map elements as an instance-level graph by decomposing them into vertices and edges to facilitate accurate and efficient end-to-end vectorized HD map learning. Furthermore, we introduce an association strategy using a Graph Neural Network to efficiently handle the complex geometry of various map elements, while maintaining scalability. Comprehensive experiments on public open dataset show that our proposed network outperforms state-of-the-art model by 1.6 mAP. We further showcase the superior scalability of our approach compared to state-of-the-art methods, achieving a 4.8 mAP improvement in long range configuration. Our code is available at https://github.com/juyebshin/InstaGraM.

Grand challenges in industrial and systems engineering

Contemporary society faces a growing set of complex issues representing significant socioeconomic, health and well-being, environmental, and sustainability challenges. The discipline of industrial and systems engineering (ISE) can play an important role in addressing these issues. This paper identifies and discusses eight grand challenges for ISE. These grand challenges are (1) Artificial Intelligence (AI) For Business and Personal Use: Decision-Making and System Design and Operations, (2) Cybersecurity and Resilience, (3) Sustainability: Environment, Energy and Infrastructure, (4) Health Issues, (5) Social Issues, (6) Logistics and Supply Chain, (7) System Integration and Operations: Humans, Automation, and AI, and (8) Industrial and Systems Engineering Education. The discussed grand challenges were derived by accomplished ISE professionals who are the authors of this paper. The implications of the ISE grand challenges for education, training, research, and implementation of ISE principles and methodologies for the benefit of global society are discussed.

An anti-occlusion vision-based method for structural motion estimation

Structural displacement is an important metric in structural health monitoring (SHM). Computer vision-based methods have been widely used for structural displacement recognition in laboratory settings. However, the movement of natural objects such as pedestrians, vehicles, and other unrelated objects, may obstruct the selected structural targets for tracking, therefore reducing the accuracy of estimated displacements. To address this challenge, this paper proposes an anti- occlusion computer vision-based method to estimate structural displacements with subpixellevel accuracy. The proposed method can be divided into three steps. First, the correlation filter is used to continuously track the selected target despite occlusion. Next, the Gaussian mixture model (GMM)-based target modeling method is proposed to identify the occluded segments in each frame. Finally, the selected target is divided into multiple patches, and the subpixel-level displacements of unobstructed patches are estimated by the subpixel patch matching algorithm. The advantages of the developed method over traditional approaches are demonstrated in simulated cases and a shaking table test of a five-story stone curtain wall structure. Additionally, the developed method is applied in a bridge construction practice.

Catalytic pyrolysis of disposable masks: Fine-tuning nickel content ratios in catalysts for enhanced hydrogen-rich gas and carbon nanomaterial quality

The widespread use and disposal of disposable masks have raised significant environmental and social concerns. In response to this issue, recent studies have explored resource recovery through the pyrolysis of discarded masks. However, limited research has been conducted on tuning the content ratios of active metal and applying various catalyst supports for targeting hydrogen (H2) and carbon nanomaterials. To address this gap, we implemented a two-zone catalytic pyrolysis system. The nickel content ratio in non-noble catalysts (Ni/Al2O3, Ni/Zeolite 4 A, and LaNiO3) was fine-tuned and applied in the catalytic reforming zone to optimize hydrogen production and graphite-like carbon nanomaterial formation. Impregnated catalysts (Ni(25)/Al2O3, Ni(25)/ Zeolite 4 A) and LaNiO3 demonstrated comparable results in terms of hydrogen concentration up to 64.6 vol% and the structural purity of carbon nanomaterial by Raman Spectroscopy, suggesting that the nickel content ratio on the catalyst plays a critical role in hydrogen production during catalytic pyrolysis. These findings are anticipated to contribute to the sustainable production of clean fuel gas products through catalytic pyrolysis, offering a viable solution to the disposal of waste masks.