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1. A cross-layer methodology for design and optimization of networks in 2.5D systems

   https://doi.org/10.1145/3240765.3240768  

   Coskun, Ayse; Eris, Furkan; Joshi, Ajay; Kahng, Andrew B.; Ma, Yenai; Srinivas, Vaishnav (November 2018, International Conference on Computer Aided Design)

   design, both inter- and intra-chiplet, impacts overall system performance as well as its manufacturing cost and thermal feasibility. This paper introduces a cross-layer methodology for designing networks in 2.5D systems. We optimize the network design and chiplet placement jointly across logical, physical, and circuit layers to achieve an energy-efficient network, while maximizing system performance, minimizing manufacturing cost, and adhering to thermal constraints. In the logical layer, our co-optimization considers eight different network topologies. In the physical layer, we consider routing, microbump assignment, and microbump pitch constraints to account for the extra costs associated with microbump utilization in the inter-chiplet communication. In the circuit layer, we consider both passive and active links with five different link types, including a gas station link design. Using our cross-layer methodology results in more accurate determination of (superior) inter-chiplet network and 2.5D system designs compared to prior methods. Compared to 2D systems, our approach achieves 29% better performance with the same manufacturing cost, or 25% lower cost with the same performance. 

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2. Chiplet-Package Co-Design For 2.5D Systems Using Standard ASIC CAD Tools

   https://doi.org/10.1109/ASP-DAC47756.2020.9045734  

   Kabir, MD Arafat; Peng, Yarui (January 2020, 2020 25th Asia and South Pacific Design Automation Conference (ASP-DAC))

   Chiplet integration using 2.5D packaging is gaining popularity nowadays which enables several interesting features like heterogeneous integration and drop-in design method. In the traditional die-by-die approach of designing a 2.5D system, each chiplet is designed independently without any knowledge of the package RDLs. In this paper, we propose a Chip-Package Co-Design flow for implementing 2.5D systems using existing commercial chip design tools. Our flow encompasses 2.5D-aware partitioning suitable for SoC design, Chip-Package Floorplanning, and post-design analysis and verification of the entire 2.5D system. We also designed our own package planners to route RDL layers on top of chiplet layers. We use an ARM Cortex-M0 SoC system to illustrate our flow and compare analysis results with a monolithic 2D implementation of the same system. We also compare two different 2.5D implementations of the same SoC system following the drop-in approach. Alongside the traditional die-by-die approach, our holistic flow enables design efficiency and flexibility with accurate cross-boundary parasitic extraction and design verification. 

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3. Highly Symmetric, Self‐Assembling 3D DNA Crystals with Cubic and Trigonal Lattices

   https://doi.org/10.1002/smll.202205830  

   Lu, Brandon; Vecchioni, Simon; Ohayon, Yoel P.; Woloszyn, Karol; Markus, Tiffany; Mao, Chengde; Seeman, Nadrian C.; Canary, James W.; Sha, Ruojie (November 2022, Small)

   Abstract The rational design of nanoscopic DNA tiles has yielded highly ordered crystalline matter in 2D and 3D. The most well‐studied 3D tile is the DNA tensegrity triangle, which is known to self‐assemble into macroscopic crystals. However, contemporary rational design parameters for 3D DNA crystals nearly universally invoke integer numbers of DNA helical turns and Watson–Crick (WC) base pairs. In this study, 24‐bp edges are substituted into a previously 21‐bp (two helical turns of DNA) tensegrity triangle motif to explore whether such unconventional motif can self‐assemble into 3D crystals. The use of noncanonical base pairs in the sticky ends results in a cubic arrangement of tensegrity triangles with exceedingly high symmetry, assembling a lattice from winding helical axes and diamond‐like tessellation patterns. Reverting this motif to sticky ends with Watson–Crick pairs results in a trigonal hexagonal arrangement, replicating this diamond arrangement in a hexagonal context. These results showcase that the authors can generate unexpected, highly complex, pathways for materials design by testing modifications to 3D tiles without prior knowledge of the ensuing symmetry. This study expands the rational design toolbox for DNA nanotechnology; and it further illustrates the existence of yet‐unexplored arrangements of crystalline soft matter. 

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4. Learning embeddings for multiplex networks using triplet loss

   https://doi.org/10.1007/s41109-019-0242-0  

   Seyedsaeed Hajiseyedjavadi, Yu-Ru Lin (January 2019, Applied network science)

   null (Ed.)

   Learning low-dimensional representations of graphs has facilitated the use of traditional machine learning techniques to solving classic network analysis tasks such as link prediction, node classification, community detection, etc. However, to date, the vast majority of these learning tasks are focused on traditional single-layer/unimodal networks and largely ignore the case of multiplex networks. A multiplex network is a suitable structure to model multi-dimensional real-world complex systems. It consists of multiple layers where each layer represents a different relationship among the network nodes. In this work, we propose MUNEM, a novel approach for learning a low-dimensional representation of a multiplex network using a triplet loss objective function. In our approach, we preserve the global structure of each layer, while at the same time fusing knowledge among different layers during the learning process. We evaluate the effectiveness of our proposed method by testing and comparing on real-world multiplex networks from different domains, such as collaboration network, protein-protein interaction network, online social network. Finally, in order to deliberately examine the effect of our model’s parameters we conduct extensive experiments on synthetic multiplex networks. 

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5. Self-supervised Feature Learning by Cross-modality and Cross-view Correspondences

   Jing, L; Zhang, L; Tian, Y (June 2021, The 4th Multimodal Learning and Applications (MULA) Workshop in conjunction with CVPR 2021)

   null (Ed.)

   The success of supervised learning requires large-scale ground truth labels which are very expensive, time- consuming, or may need special skills to annotate. To address this issue, many self- or un-supervised methods are developed. Unlike most existing self-supervised methods to learn only 2D image features or only 3D point cloud features, this paper presents a novel and effective self-supervised learning approach to jointly learn both 2D image features and 3D point cloud features by exploiting cross-modality and cross-view correspondences without using any human annotated labels. Specifically, 2D image features of rendered images from different views are extracted by a 2D convolutional neural network, and 3D point cloud features are extracted by a graph convolution neural network. Two types of features are fed into a two-layer fully connected neural network to estimate the cross-modality correspondence. The three networks are jointly trained (i.e. cross-modality) by verifying whether two sampled data of different modalities belong to the same object, meanwhile, the 2D convolutional neural network is additionally optimized through minimizing intra-object distance while maximizing inter-object distance of rendered images in different views (i.e. cross-view). The effectiveness of the learned 2D and 3D features is evaluated by transferring them on five different tasks including multi-view 2D shape recognition, 3D shape recognition, multi-view 2D shape retrieval, 3D shape retrieval, and 3D part-segmentation. Extensive evaluations on all the five different tasks across different datasets demonstrate strong generalization and effectiveness of the learned 2D and 3D features by the proposed self-supervised method. 

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