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With the rapid advancement of DNNs, numerous Process-in-Memory (PIM) architectures based on various memory technologies (Non-Volatile (NVM)/Volatile Memory) have been developed to accelerate AI workloads. Magnetic Random Access Memory (MRAM) is highly promising among NVMs due to its zero standby leakage, fast write/read speeds, CMOS compatibility, and high memory density. However, existing MRAM technologies such as spin-transfer torque MRAM (STT-MRAM) and spin-orbit torque MRAM (SOT-MRAM), have inherent limitations. STT-MRAM faces high write current requirements, while SOT-MRAM introduces significant area overhead due to additional access transistors. The new STT-assisted-SOT (SAS) MRAM provides an area-efficient alternative by sharing one write access transistor for multiple magnetic tunnel junctions (MTJs). This work presents the first fully digital processing-in-SAS-MRAM system to enable 8-bit floating-point (FP8) neural network inference with an application in on-device session-based recommender system. A SAS-MRAM device prototype is fabricated with 4 MTJs sharing the same SOT metal line. The proposed SAS-MRAM-based PIM macro is designed in TSMC 28nm technology. It achieves 15.31 TOPS/W energy efficiency and 269 GOPS performance for FP8 operations at 700 MHz. Compared to state-of-the-art recommender systems for the same popular YooChoose dataset, it demonstrates a 86 ×, 1.8 ×, and 1.12 × higher energy efficiency than that of GPU, SRAM-PIM, and ReRAM-PIM, respectively. 

Utilizing the phase-matching conditions of inter-modal four-wave mixing in an elliptical-core few-mode fiber supporting three non-degenerate modes, we experimentally demonstrate schemes for generating orbital-angular-momentum (OAM)-entangled photon pairs with high mode purity and for achieving highly mode-selective frequency conversion of beams in OAM-compatible (LP11a, LP11b) mode basis. These techniques expand the toolbox for using OAM modes in both classical and quantum communications and information processing. 

Abstract Multimodal integration combines information from different sources or modalities to gain a more comprehensive understanding of a phenomenon. The challenges in multi-omics data analysis lie in the complexity, high dimensionality, and heterogeneity of the data, which demands sophisticated computational tools and visualization methods for proper interpretation and visualization of multi-omics data. In this paper, we propose a novel method, termed Orthogonal Multimodality Integration and Clustering (OMIC), for analyzing CITE-seq. Our approach enables researchers to integrate multiple sources of information while accounting for the dependence among them. We demonstrate the effectiveness of our approach using CITE-seq data sets for cell clustering. Our results show that our approach outperforms existing methods in terms of accuracy, computational efficiency, and interpretability. We conclude that our proposed OMIC method provides a powerful tool for multimodal data analysis that greatly improves the feasibility and reliability of integrated data. 

Photonic technologies continue to drive the quest for new optical materials with unprecedented responses. A major frontier in this field is the exploration of nonlocal (spatially dispersive) materials, going beyond the local, wavevector-independent assumption traditionally adopted in optical material modeling. The growing interest in plasmonic, polaritonic, and quantum materials has revealed naturally occurring nonlocalities, emphasizing the need for more accurate models to predict and design their optical responses. This has major implications also for topological, nonreciprocal, and time-varying systems based on these material platforms. Beyond natural materials, artificially structured materials—metamaterials and metasurfaces—can provide even stronger and engineered nonlocal effects, emerging from long-range interactions or multipolar effects. This is a rapidly expanding area in the field of photonic metamaterials, with open frontiers yet to be explored. In metasurfaces, in particular, nonlocality engineering has emerged as a powerful tool for designing strongly wavevector-dependent responses, enabling enhanced wavefront control, spatial compression, multifunctional devices, and wave-based computing. Furthermore, nonlocality and related concepts play a critical role in defining the ultimate limits of what is possible in optics, photonics, and wave physics. This Roadmap aims to survey the most exciting developments in nonlocal photonic materials and metamaterials, highlight new opportunities and open challenges, and chart new pathways that will drive this emerging field forward—toward new scientific discoveries and technological advancements.