Multimodal summarization with multimodal output (MSMO) aims to generate coherent textual summaries while selecting the most semantically relevant images to enhance expressiveness. Despite the advancements of large multimodal models like GPT-4o, LLaMA-3, and Grok-3, these models often exhibit hallucination and weak visual-text alignment when applied to MSMO tasks. To address these challenges, we propose ModalSyncSum, a unified framework that enhances semantic consistency and visual faithfulness. It incorporates image-aware information extraction to mitigate visual-text misalignment, QA-based description verification to detect and correct hallucinated image descriptions, and named entity-guided refinement to ensure factual accuracy and entity alignment across modalities. Furthermore, we introduce a new evaluation metric M3AS, which jointly considers image content coverage, text-image alignment, and summary consistency, filling the gap in evaluating multimodal summary quality. Experimental results show that our model outperforms prompt-based baselines across multiple datasets, achieving significant gains on ROUGE, BLEU, and BERTScore, with BLEU improving by 21.95%. In human evaluation, M3AS exhibits stronger correlation with human judgments in consistency, image-summary relevance, and focus, surpassing existing automatic metrics.

Aspect-based sentiment analysis (ABSA) aims to identify aspect terms and determine their sentiment polarity. While dependency trees combined with contextual semantics provide structural cues, existing approaches often rely on dotproduct similarity and fixed graphs, which limit their ability to capture nonlinear associations and adapt to noisy contexts. To address these limitations, we propose the Optimal TransportEnhanced Syntactic-Semantic Graph Network (OTESGN), a model that jointly integrates structural and distributional signals. Specifically, a Syntactic Graph-Aware Attention module models global dependencies with syntax-guided masking, while a Semantic Optimal Transport Attention module formulates aspect-opinion association as a distribution matching problem solved via the Sinkhorn algorithm. An Adaptive Attention Fusion mechanism balances heterogeneous features, and contrastive regularization enhances robustness. Extensive experiments on three benchmark datasets (Rest14, Laptop14, and Twitter) demonstrate that OTESGN delivers state-of-the-art performance. Notably, it surpasses competitive baselines by up to +1.30 Macro-F1 on Laptop14 and +1.01 on Twitter. Ablation studies and visualization analyses further highlight OTESGN's ability to capture finegrained sentiment associations and suppress noise from irrelevant context.

To establish flatten networks and accomplish rapid and efficient communications in the future hyper-scale data centers, HERO, a high-radix optical switch based on integrated silicon photonics, is proposed in this work. The architecture of HERO, including the switch fabric, switch interface, and switch controller, is described in detail. Two new switch control approaches: 1) split-transaction predictive control and 2) wavelength-group switching, are developed. The efficient control together with the optimized high-radix integrated optical switch fabrics help HERO achieve over 1 Pbps switching capacity. Even for small packets, such as 64–256 B Ethernet packets, the maximal utilization of the switch can reach up to 83%, and the throughput can approximate 1 Pbps. Further design explorations on the packet length and some key configurations, including the number of wavelengths and wavelength groups, are also conducted in this work, paving the way to the design of high-performance flatten data-center networks in the future.

Silicon photonics is the leading candidate technology for high-speed and low-energy-consumption networks. Thermal and process variations are the two main challenges of achieving high-reliability photonic networks. Thermal variation is due to the heat issues created by application, floorplan, and environment, while process variation is caused by fabrication variability in the deposition, masking, exposition, etching, and doping. Tuning techniques are then required to overcome the impact of the variations and efficiently stabilize the performance of silicon photonic networks. We extend our previous optical switch integration model, BOSIM, to support the variation and thermal analyses. Based on device properties, we propose indirect feedback tuning (IFT) to simultaneously alleviate thermal and process variations. IFT can improve the BER of silicon photonic networks to 10-9under different variation situations. Compared to state-of-the-art techniques, IFT can achieve an up to 1.52 ×108times bit-error-rate improvement and 4.11X better heater energy efficiency. Indirect feedback does not require high-speed optical signal detection, and thus, the circuit design of IFT saves up to 61.4% of the power and 51.2% of the area compared to state-of-the-art designs.

With the increasing popularity of data-intensive applications in data centers, the switching fabric in the internode network becomes significant. Silicon-photonic switching fabrics have a bright future in data centers, which offer high bandwidth, high energy efficiency, and low latency. However, integrating a high radix multistage switching fabric in a single chip faces challenges. A large number of waveguide crossings on the silicon photonic die causes massive power loss and introduces a tremendous amount of crosstalk noise. In this article, we propose a chip partition optimization platform (POP), which can decrease the number of waveguide crossings and shorten the on-chip traversal distance of optical signals. Our algorithms can effectively reduce the power loss and crosstalk noise in silicon-photonic multistage switching fabrics, and help to improve the signal integrity. For example, compared with the common design, POP can achieve 33-dB improvement on average power loss, 42-dB improvement on the worst-case power loss, and 39-dB improvement on the worst-case signal to noise ratio, in a$1024\times1024$butterfly based silicon-photonic switching fabric.

A lot of efforts have been devoted to optically enabled high-performance communication infrastructures for future manycore processors. Silicon photonic network promises high bandwidth, high energy efficiency and low latency. However, the ever-increasing network complexity results in high optical power demands, which stress the optical power delivery and affect delivery efficiency. Facing these challenges, we propose Ring-based Optical Active Delivery (ROAD) system, to effectively manage and efficiently deliver high optical power throughout photonic-electronic hybrid systems. Experimental results demonstrate up to 5.49X energy efficiency improvement compared to traditional design without affecting processor performance.

Silicon photonic networks are revolutionizing computing systems by improving the energy efficiency, bandwidth, and latency of data movements. Optical modulators, such as microresonators (MRs) and Mach–Zehnder interferometers (MZIs), are the basic building blocks of silicon photonic networks. This paper proposes a SPICE-compatible electro-optical co-simulation model, basic optical switch integration model (BOSIM), to systematically study optical modulators using PN, PIN, and metal–insulator–silicon (MIS) capacitor device technologies. BOSIM holistically models both transient and steady state properties, such as switching speed, power, transmission spectrum, area, and carrier distribution. BOSIM is validated by the measured data from eight research groups and companies. Compared to MRs, BOSIM shows MZIs are fast, with a high extinction ratio and large bandwidth but in the sacrifice of loss, energy, and area. Using a PIN diode over a PN diode can save area, but retain the loss and energy, while an MIS capacitor has the shock response of carrier distribution in a narrow range and is marginalized gradually. For instance, an MZI can achieve a$2.5 {\times }$bit rate,$6.06{\times }$extinction ratio,$71.04 {\times }\,\,3$-dB bandwidth, but costs at least$1.93 {\times }$passing loss,$1.46 {\times }$energy consumption, and$16.67 {\times }$area, compared with MR.

While many new applications prefer manycore processor with a large number of cores, the exploding communications among multiple cores, caches, and off-chip memories is posing a fundamental challenge on manycore designs. Silicon photonics-based interconnection network promises high bandwidth, low latency, and high energy efficiency, and can potentially meet the communication requirements of manycore processors. In this paper, we propose CAMON, a small low-cost silicon photonic chiplet integrated into the manycore processor package. CAMON chiplet can effectively alleviate the communication bottlenecks of manycore processors and improve the energy efficiency of data movement, especially for large-scale systems. We develop a distributed arbitration system, a low-power low-latency optical interface, and an off-chip laser preactivation mechanism for CAMON. The experimental results show that compared with the electrical network, CAMON can improve the full-system performance per energy by 4.6×, speedup the manycore processor by 2.7×, and save the area of the processor die by 3%, in a 512-core system.

The advancement of silicon photonics promises integrated optical switches to provide high-bandwidth, low-latency, and low-power communications in data centers. An optical switch’s loss limits its scale and affects the energy efficiency of the switch system. In this paper, we present cross-layer optical switch optimization (CLOSO), a cross-layer optimization (CLO) framework, based on not only photonic device models at the physical layer but also optical switch models at the fabric layer. With the proposed framework, optimal losses of optical switches can be evaluated efficiently, and the corresponding losses and design parameters of photonic devices can be obtained. Using CLOSO, we optimize four categories of integrated optical switches, Crossbar, PILOSS, DRAGON, and FODON, and compare them regarding their optimal worst-case loss with variation of the switch scale and data rate of signals. Furthermore, system-level evaluations of the optimized optical switches are performed, demonstrating a significant improvement of energy efficiency from the CLO. For instance, CLOSO helps to reduce the energy consumption of a 64-port DRAGON and FODON to as low as 6 pJ/bit and that of a 128-port DRAGON and FODON to as low as 10 pJ/bit. The investigation of 128-port switches also shows the necessity of adaptive power control on lasers for high-radix integrated optical switches. Through quantitative analyses and comparisons, CLOSO shows the capability of facilitating initial design exploration of optical switches and paves the way to fair evaluations and comparisons of switch systems in data centers.

Rack-scale computing systems are promising to undertake the emerging large-scale applications by distributing massive tasks to processing cores. The communication and coordination efficiency of these tasks and resources directly affect the system performance and energy consumption. Silicon photonic interconnects are expected to address the communication and system power consumption challenges imposed on rack-scale systems. However, the control for optical interconnects can cause server performance degradation if not properly designed, especially for the complicated and time-consuming multidomain networks. In this paper, we study the optical interconnects for rack-scale computing systems and propose a new communication flow and control scheme for the efficient coordination of distributed resources. Particularly, we first propose a forward propagation strategy that parallels the path reservation process with the distributed tasks connection setup. Second, we develop a pre-emptive chain feedback (PCF) scheme to optimize multidomain path reservation. The PCF scheme pre-emptively allocates network resources with the help of multicell reservation window and quickly releases resources with a feedback mechanism. This solution increases the network resources utilization and task coordination efficiency while minimizing path reservation overheads. Comparing to the baseline InfiniBand network fabric and handshake scheme, PCF can improve network throughput greatly under uniform and hotspot traffic patterns. Realistic benchmark results show that the PCF scheme on average reduces 52% and 60% energy consumption per unit system performance than InfiniBand and the handshake scheme for a 256-node rack system.

High-radix integrated silicon photonic switches promise ultrahigh bandwidth communications required by next generation data centers. To holistically explore the characteristics of high-radix integrated optical switches, this work systematically studies the latency, throughput and energy consumption, with detailed models and various system configurations. Three categories of space switches, blocking, rearrangeable non-blocking and strictly non-blocking switches, are investigated, together with one of the widely used wavelength switches, arrayed waveguide grating router (AWGR). The work paves the ways to automatically optimize high-radix integrated silicon photonic switches.

In recent years, optical interconnection networks have been proposed in order to achieve the ultrahigh bandwidth and low latency requirements for inter/intra-chip communication. In these optical interconection networks, series of basic optical elements are employed. Via these series of optical elements, the intrinsic crosstalk noise is generated. With a large scale of these optical elements, the signal-to-noise ratio (SNR) of an optical interconnect can be reduced by this crosstalk noise. In this paper, we utilize the adaptive power control (APC) to enhance the SNR under the crosstalk noise constraints. APC has been known to save energy and reduce power consumption. We apply this technique in one of the inter/intra-chip optical interconnect called I2CON. A new cluster design, namely the Beam cluster, is also introduced. Results have demonstrated that the APC can help to reduce crosstalk noise, hence, the overall SNR is improved. Comparison results have also indicated the further improvement of SNR in I2CON using Beam cluster when APC is applied.

The increasing demand for more computational power from scientific computing, big data processing, and machine learning is pushing the development of HPC (high-performance computing) systems. As the basic HPC building blocks, modularized server racks with a large number of multicore nodes are facing performance and energy efficiency challenges. This paper proposes RSON, an optical network for rack-scale computing systems. RSON connects processor cores, caches, local memories, and remote memories through a novel inter/intra-chip silicon photonic network architecture. We develop a low-latency scalable channel partition and low-power dynamic path priority control scheme for RSON. Experimental results show that RSON can help rack-scale computing systems achieve up to 6.8X higher performance under the same energy consumption than state-of-the-art systems under the latest APEX (application performance at extreme scale) benchmarks.

Maintaining high energy efficiency has become a critical design issue for high-performance systems. Many power management techniques have been proposed for the processor cores such as dynamic voltage and frequency scaling (DVFS). However, very few solutions consider the power losses suffered on the power delivery system (PDS), despite the fact that they have a significant impact on the system overall energy efficiency. With the explosive growth of system complexity and highly dynamic workloads variations, it is challenging to find the optimal power management policies which can effectively match the power delivery with the power consumption. To tackle the above problems, we propose a reinforcement learning-based power management scheme for manycore systems to jointly monitor and adjust both the PDS and the processor cores aiming to improve system overall energy efficiency. The learning agents distributed across power domains not only manage the power states of processor cores but also control the on/off states of on-chip VRs to proactively adapt to the workload variations. Experimental results with realistic applications show that when the proposed approach is applied to a large-scale system with a hybrid PDS, it lowers the system overall energy-delay-product (EDP) by 41% than a traditional monolithic DVFS approach with a bulky off-chip VR.

Energy-efficiency is becoming increasingly important to modern computing systems with multi-/many-core architectures. Dynamic Voltage and Frequency Scaling (DVFS), as an effective low-power technique, has been widely applied to improve energy-efficiency in commercial multi-core systems. However, due to the large number of cores and growing complexity of emerging applications, it is difficult to efficiently find a globally optimized voltage/frequency assignment at runtime. In order to improve the energy-efficiency for the overall multicore system, we propose an online DVFS control strategy based on core-level Modular Reinforcement Learning (MRL) to adaptively select appropriate operating frequencies for each individual core. Instead of focusing solely on the local core conditions, MRL is able to make comprehensive decisions by considering the running-states of multiple cores without incurring exponential memory cost which is necessary in traditional Monolithic Reinforcement Learning (RL). Experimental results on various realistic applications and different system scales show that the proposed approach improves up to 28% energy-efficiency compared to the recent individual-RL approach.

The memory wall problem is due to the imbalanced developments and separation of processors and memories. It is becoming acute as more and more processor cores are integrated into a single chip and demand higher memory bandwidth through limited chip pins. Optical memory interconnection network (OMIN) promises high bandwidth, bandwidth density, and energy efficiency, and can potentially alleviate the memory wall problem. In this paper, we propose an optical inter/intra-chip processor-memory communication architecture, called MOCA. Experimental results and analysis show that MOCA can significantly improve system performance and energy efficiency. For example, comparing to Hybrid Memory Cube (HMC), MOCA can speedup application execution time by 2.6x, reduce communication latency by 75%, and improve energy efficiency by 3.4x for 256-core processors in 7 nm technology.

Recent advances in photonics technologies have made optical interconnection network an attractive option for computing systems from high-performance computers and data centers to automobiles and cellphones. Optical interconnection network promises ultra-high bandwidth, low latency, and great energy efficiency to alleviate the inter-rack, intra-rack, intraboard, and intra-chip communication bottlenecks in multiprocessor systems. Silicon-based photonics technologies piggyback onto developed silicon fabrication processes to provide viable and cost-effective solutions. Both industry and academia have invested significant efforts to develop and commercialize optical interconnection network technologies. This paper reviews the latest progresses and provides insights into the challenges and future developments.