Subset selection under budget constraints is critical in applications like multi-robot patrolling, crime deterrence, and targeted marketing, where multiple agents must jointly select targets and plan feasible routes. We formalize this challenge as Multi-Subset Selection with Budget-Constrained Routing (MSS-BCR), involving complex, non-additive cost structures that defy traditional methods. We propose GRIP, a graph-based framework integrating spatial reward fields and policy learning to enable coordinated, budget-aware target selection and routing. GRIP uses attention-based embeddings and constraint-triggered pruning with utility recovery to produce high-quality, feasible solutions. Experiments based on multiple synthetic and real-world datasets show GRIP outperforms baselines in reward efficiency and scalability across varied scenarios.

Instant delivery has become an essential service in daily life, requiring strict delivery timelines. However, traditional delivery methods that employ human couriers struggle to meet the soaring delivery demands due to labor shortages. While researchers have explored alternative solutions using ground vehicles (e.g., crowdsourced taxis) and Unmanned Aerial Vehicles (UAVs), their inherent limitations, such as constrained delivery detour for crowdsourced taxis and limited battery capacity of UAVs, greatly constrain their effectiveness. To address these challenges, this paper proposes a novel air-ground delivery paradigm that cooperatively integrates UAVs and crowdsourced taxis. First, UAV stations are strategically deployed based on delivery gaps between the delivery demands and taxis' delivery capacity, instead of delivery demands only; Then, a predictive UAV repositioning strategy is designed to bridge instantaneously dynamic delivery gaps. Thereafter, a transfer learning-based (TL-based) algorithm that mines the delivery knowledge of human couriers is designed to optimize the cooperative performance. This algorithm extracts behavioral insights from human couriers and transfers them to enhance the delivery capabilities of UAVs and taxis. Finally, parcel assignment is formulated as optimization problems aimed at maximizing total preferences of UAVs and taxis, and maximizing delivery number while minimizing cost, respectively. Evaluations on real-world datasets demonstrate that the proposed method delivers 27.4% more parcels, saves 19.2% delivery cost, and preserves 36.3% more of the travel experience of taxi passengers than the state-of-the-art (SOTA) air-ground cooperative approach for instant delivery.

Persuasion, a vital social skill, influences beliefs, attitudes, and behaviors through conversation. Yet, current dialogue agents either rely on scenario-specific strategies, restricting their cross-context adaptability, or neglect persuasion’s logical structure. They focus on isolated strategy classification, overlooking the significance of fine-grained sequential planning for real-world scenarios. To address these limitations, inspired by basic human mental activities, we present PersuHSG, an adaptive persuasion strategy planning framework. The core idea is to conceptualize persuasion as a tripartite framework comprising cognition, affection, and volition, with each stage represented as a graph layer and principle-based strategies for efficient multi-stage persuasion. Specifically, we first develop PersuInstruct, a fine-tuning dataset to improve dialogue agents’ strategic planning and response generation. Then, we propose a graph-aware planning algorithm for stage-strategy-response reasoning to generate persuasive responses for diverse scenarios. Extensive experiments confirm that PersuHSG significantly enhances the persuasiveness of Large Language Models (LLMs), allows smaller models (e.g., 9B, 13B) to achieve competitive performance, and demonstrates the efficacy of structured strategy planning in improving model efficiency and adaptability.

Dynamic graph representation learning has emerged as a pivotal paradigm for modeling time-varying relational patterns in complex systems ranging from social networks to urban mobility. While existing methods achieve notable progress in temporal modeling, one critical challenge remains insufficiently addressed: identifying dual temporal evolution, i,e., instantaneous states and evolutionary trajectories. To address the challenge, we propose HMGNN, a novel dynamic graph learning framework that harmoniously integrates temporal dynamics modeling with stable structural representation learning, allowing adaptive pattern discovery while preserving feature consistency in evolving environments. Firstly, we propose a dynamic model that integrates a historical information perception module and a temporal aggregation module. The module converts the historical information into the model and adaptively measures the impact of the instantaneous and historical information effectively through the aggregation function. Secondly, we devise a dual-component model learning framework comprising contrastive learning and multi-granular temporal curriculum learning to holistically capture evolutionary dynamics. The contrastive learning component employs continuous-view contrastive alignment to preserve stable node feature across temporal evolution. Complementarily, our multi-granular temporal curriculum learning introduces masking mechanism to explicitly learn different time interval evolution patterns. Extensive experiments demonstrate the significant superiority of HMGNN against state-of-the-art dynamic graph learning methods in terms of all evaluation metrics.

Abstract Persuasion, as one of the crucial abilities in human communication, has garnered extensive attention from researchers within the field of intelligent dialogue systems. Developing dialogue agents that can persuade others to accept certain standpoints is essential to achieving truly intelligent and anthropomorphic dialogue systems. Benefiting from the substantial progress of Large Language Models (LLMs), dialogue agents have acquired an exceptional capability in context understanding and response generation. However, as a typical and complicated cognitive psychological system, persuasive dialogue agents also require knowledge from the domain of cognitive psychology to attain a level of human-like persuasion. Consequently, the cognitive strategy-enhanced persuasive dialogue agent (defined as CogAgent ), which incorporates cognitive strategies to achieve persuasive targets through conversation, has become a predominant research paradigm. To depict the research trends of CogAgent, in this paper, we first present several fundamental cognitive psychology theories and give the formalized definition of three typical cognitive strategies, including the persuasion strategy, the topic path planning strategy, and the argument structure prediction strategy. Then we propose a new system architecture by incorporating the formalized definition to lay the foundation of CogAgent. Representative works are detailed and investigated according to the combined cognitive strategy, followed by the summary of authoritative benchmarks and evaluation metrics. Finally, we summarize our insights on open issues and future directions of CogAgent for upcoming researchers.

Unmanned Aerial Vehicles (UAVs) are emerging as a pivotal component in the field of intelligent transportation systems. Leveraging virtual-physical interactions, digital twin technology significantly enhances the adaptability of UAVs in complex traffic environments. However, current approaches still pose three major challenges: contextual adaptability, timely responsiveness, and effective multi-UAV coordination. In this paper, we introduce EnFlexiTwin, a digital twin enhancement assistance platform seamlessly integrated with AdaSor, a lightweight adaptive data selector. EnFlexiTwin automates the construction of incremental learning datasets, enabling real-time enhancements that allow digital twins to adapt to new time series data while preserving historical knowledge. We test EnFlexiTwin on a real-world dataset from low-altitude small-parcel delivery. The results show improved performance and adaptability of digital twins. Furthermore, time-varying simulations on real-world dataset and experiments on a practical air-ground cooperative UAV swarm application highlight that EnFlexiTwin achieves superior enhancements under varying real-time requirements and swarm scale compared to baseline approaches.

Multiagent routing problems, arising from practical applications, such as logistics, transportation, and emergency response, face challenges due to the exponential growth of the search space with increasing problem scales. This article proposes RouteMaker to address the often-overlooked multiagent routing problems involving dedicated multiple depots. RouteMaker leverages role-interaction-based graph neural network (RIGNN) to realize effective locations assignments and integrates an advanced planner to plan travel path for each agent. RouteMaker is trained on small-scale problems and can produce comparable or superior approximate optimal solutions compared with the best heuristic baselines. Notably, the learned RouteMaker generalizes seamlessly to large-scale problems and real-world problems without the need for fine-tuning, delivering significantly higher quality solutions in relatively less time. For scenarios involving 40 agents and 1000 locations, RouteMaker achieves over $600\times $ speed improvement and more than 88% cost reduction, compared with the representative classical heuristic solver (ORTools).

The Industrial Internet of Things (IIoT) has become a critical technology to accelerate the process of digital and intelligent transformation of industries. As the cooperative relationship between smart devices in IIoT becomes more complex, obtaining deterministic responses of IIoT periodic time-critical computing tasks becomes a crucial and nontrivial problem. However, few current works in cloud/edge/fog computing focus on this problem. This paper is a pioneer in exploring deterministic scheduling and network structural optimization problems for IIoT periodic time-critical computing tasks. We first formulate the two problems and derive theorems to help quickly identify computation and network resource sharing conflicts. Based on this, we propose a deterministic scheduling algorithm,IIoTBroker, which realizes a deterministic response for each IIoT task by optimizing the fine-grained computation and network resources, and a network optimization algorithm,IIoTDeployer, which provides a cost-effective structural upgrade solution for existing IIoT networks. Our methods are illustrated to be cost-friendly, scalable, and deterministic response guaranteed with low computation cost from our simulation results.

Instant delivery has become a fundamental service in people's daily lives. Different from the traditional express service, the instant delivery has a strict shipping time constraint after being ordered. However, the labor shortage makes it challenging to realize efficient instant delivery. To tackle the problem, researchers have studied to introduce vehicles (i.e., taxis) or Unmanned Aerial Vehicles (UAVs or drones) into instant delivery tasks. Unfortunately, the delivery detour of taxis and the limited battery of UAVs make it hard to meet the rapidly increasing instant delivery demands. Under this circumstance, this paper proposes an air-ground cooperative instant delivery paradigm to maximize the delivery performance and meanwhile minimize the negative effects on the taxi passengers. Specifically, a data-driven delivery potential-demands-aware cooperative strategy is designed to improve the overall delivery performance of both UAVs and taxis as well as the taxi passengers' experience. The experimental results show that the proposed method improves the delivery number by 30.1% and 114.5% compared to the taxi-based and UAV-based instant delivery respectively, and shortens the delivery time by 35.7% compared to the taxi-based instant delivery.

Electric vehicles (EVs), which have become one of the most important commuting vehicles in the world, heavily depend on the support of robust and efficient grids. Many researchers focus on optimizing the charging of EVs by the grid. However, they overlook the emergency charging when the large-scale grid fails. Inspired by the DC-2-DC technology, EVs can achieve vehicle-2-vehicle (V2V) energy sharing between each other, which inspires us to study leveraging the significant number of electric buses (e-buses) as effective and cost-efficient standby energy to charge low-power EVs when a large-scale grid outage occurs, whereas the feasibility, scheduling algorithm, and performance of e-bus-based V2V energy sharing remain open questions. With this in mind, we first reveal that it is feasible for e-buses to serve as effective standby power for low-power EVs, after careful analysis of real-world data. Then, we conduct a data-driven estimation of the energy consumption to determine the volume of the V2V shared energy for large-scale EVs. Accordingly, we formulate the e-bus-based V2V energy sharing problem, whose two objectives are to maximize the total number of the sufficiently charged EVs and to minimize the distance of these EVs moving to the e-bus for charging. The first objective is to charge more low-power EVs with the limited energy volume of the e-buses and the second objective is to reduce the range anxiety of the low-power EV drivers. Observing that existing algorithm is time-consuming or inefficient, which cannot be applied for the problem instance with a large number of e-buses and EVs, we design a low-complexity approximation algorithm for the problem. Both theoretical analysis and comprehensive data-driven evaluation demonstrate that the proposed algorithm, 1) significantly optimizes the optimization objectives by 23.6% and 15.8% on average, and up to 34.8% and 34.0% in extreme cases, compared with the existing approximation algorithm and 2) achieves over$100\times $execution speed improvement compared to the linear programming (LP)-based algorithm and evolutionary learning (EL) algorithms. The results demonstrate that the proposed algorithm achieves a better overall performance and makes it a promising solution for large-scale problem instances.

Unmanned Aerial Vehicles (UAVs) are widely applied in smart city applications such as urban sensing and delivery, due to the UAVs’ agility, low cost and not being restricted by ground road conditions. However, the limited battery capacity becomes one of the biggest obstacles to the application of UAVs. To address this issue, this paper investigates an emergency response application, in which UAVs generally ride crowdsourced buses to save energy and respond to a stochastic emergency event (such as a traffic accident) when the event occurs. For the bus-based UAV response paradigm, a single UAV response process with the constraint of the bus mobility is first modeled. Subsequently, a data-driven UAV path planning algorithm is designed. Then two emergency response cases by multi-UAV are investigated. One case is irregular emergency response, whose objective is to maximize the temporal-spatial coverage of the urban area. The other case is predictable emergency response, which optimizes the response performance to these emergencies. Thereafter, the bus-stimulating problems for the two cases are formulated and solved. Finally, utilizing a real-world bus trajectory dataset generated by a large-scale bus fleet and a traffic event dataset, the emergency response performance of the bus-based UAV response paradigm is comprehensively evaluated. The results show that (1) with only 30 UAVs, 90% of Shenzhen city can be covered in the irregular emergency response case; (2) with only 50 UAVs, the average response delay to the emergencies is shorter than 1.5 minutes, which is 56% shorter than baselines, in the predictable emergencies response case.

Multiedge cooperative computing that combines constrained resources of multiple edges into a powerful resource pool has the potential to deliver great benefits, such as a tremendous computing power, improved response time, and more diversified services. However, the mass heterogeneous resources composition and lack of scheduling strategies make the modeling and cooperating of multiedge computing system particularly complicated. This article first proposes a system-level state evaluation model to shield the complex hardware configurations and redefine the different service capabilities at heterogeneous edges. Second, an integer linear programming model is designed to cater for optimally dispatching the distributed arriving requests. Finally, a learning-based lightweight real-time scheduler, CoRaiS is proposed. CoRaiS embeds the real-time states of the multiedge system and requests information, and combines the embeddings with a policy network to schedule the requests, so that the response time of all requests can be minimized. Evaluation results verify that the CoRaiS can make a high-quality scheduling decision in real-time, and can be generalized to other multiedge computing system, regardless of the system scales. Characteristic validation also demonstrates that the CoRaiS successfully learns to balance loads, perceive real-time state and recognize heterogeneity while scheduling.

Large-scale social events like civil unrest, distinctly impacts our daily life. For this reason, it is essential to predict specific events in advance based on relevant information. Studies on data-driven event prediction assume that there are precursors for predictable events that can be tracked in history. Therefore, modeling prior evolution of a target event appropriately using relevant information or implicit indicators is of great importance to anticipate whether concerned events are likely to occur sometime in the future. However, there are issues among existing relevant studies: (I) how to properly define event data for training to match the realistic prediction scenario. (II) how to extract useful previous information from available data flow and model relevant evolution or dynamic feature of events reasonably. (III) it is both practical and urgent to mine precursors or clues from spatial-temporal data for interpreting prediction results. In this paper, we propose a novel feature learning framework for event prediction that can discover potential precursors from the input data. The prediction model primarily consists of a graph encoder module using GNN (Graph Neural Network) techniques and a temporal feature learning module employing attention mechanism. Meanwhile, we develop a backward tracking method to model the previous evolution of an event by retrieving prospective relevant events in the past. Multiple experiments conducted on datasets collected from various regions in the real world demonstrate appreciable performance of our proposed model in social event prediction task.

Physical layer cross-technology communication (PHY-CTC) opens new horizons for spectrum utilization and wireless cooperation in the crowded ISM band. Current PHY-CTC technologies can be divided into two categories: one aims for high communication reliability, but sacrifices compatibility with commodity devices and the other maintains compatibility but suffers dramatically limited reliability. The latter mainly leverages the WiFi OFDM signal to emulate the ZigBee signal, while the Cyclic Prefix (CP) in OFDM brings inevitable signal disturbance and errors. To address these issues, we present WibZig, the first WiFi-To-ZigBee PHY-CTC technology that achieves high reliability and is fully compatible with commodity devices. By carefully selecting a cluster of CCK codewords that exhibit similar phase characteristics to ZigBee chips, we can emulate any given ZigBee symbol with great accuracy. In addition, WibZig adaptively controls the first CCK codeword of each cluster to eliminate inter-cluster phase discontinuity when emulating a ZigBee packet with multiple clusters. WibZig requires no hardware modification and is compatible with most commodity devices. We implement WibZig on both USRP and commercial devices and conduct extensive evaluations under various settings, which demonstrate a 15x improvement in reliability and a 7x increase in range compared to the latest PHY-CTC work.

The battery-powered Unmanned Aerial Vehicle (UAV) is a promising alternative to traditional logistics trucks. Using UAVs can achieve much more speedy, cost-effective, and environment-friendly delivery on an urban scale. However, UAVs suffer from insufficient delivery range and battery aging. This paper presents an innovative logistics UAV scheduling framework using public buses, in which logistics UAVs Land and Recharge its battery on Buses (ULRB) to extend its delivery range and decelerate its fading battery capacity. This work correlates physical layer parameters such as the energy consumption rate, the parcels weight, UAV velocity, the battery temperature to the UAVs path planning, the battery discharging, and the capacity fading models. Specifically, the ULRB framework consists of a single-UAV scheduling module and a multi-UAV dispatching module. In the single-UAV module, a Markov-based algorithm is utilized to plan the UAVs flying path to land and dynamically get recharged on the bus. The latter module optimized the delivery progress in a multi-UAV, multi-parcel, and multi-bus scenario. Finally, using a large-scale real-world bus trajectory dataset, extensive evaluations are conducted to verify ULRB. The results show that ULRB can extend the UAVs delivery range by 5.54 and decelerate the battery aging by 3.26 on average.

Parcel delivery and Point of Interest (PoI) surveillance are two fundaches were conducted for an isolated application, separately. UAV-sharing in the two heterogeneous applications bears significant but unexplored benefit potentials, similar to current sharingmental applications of Unmanned Aerial Vehicles (UAVs) in city. Traditional resear economy such as taxi sharing. However, the inconsistency of the two heterogeneous applications in both temporal and spatial domains would impact the sharing performance. This work illustrates the first quantified studies of the UAV-sharing performance in the two heterogeneous applications. Specifically, some critical constraints of the UAV-sharing process are first discussed. Thereafter, a data-driven evaluation is conducted to understand the sharing process of the UAVs with a delivery dataset and a traffic accident dataset obtained from Shanghai city. Some inspiring results verify the particularly excellent UAV-sharing performance in the two heterogeneous applications.

Unmanned Aerial Vehicles (UAVs) are widely employed in smart city. However, the limited battery capacity is one of the UAV's most critical obstacles to monitoring mission. Recently, leveraging public buses to relay UAVs has been shown to be a promising solution to this critical issue. Existing works on this solution focused on pre-determined scenarios, namely the mission time and location of the UAVs are determined in advance. While in on-demand mission such as emergency response, mission time and location of the UAV is on-demand and stochastic. How the UAV riding on a bus perform in such stochastic missions remains open. In this paper, driven by the bus mobility data, a sampling-based algorithm is designed to navigate UAVs to land on buses in on-demand applications. A simple greedy algorithm is proposed to determine the appropriate buses to relay UAVs, so that the scheduling performance of the UAVs is optimized. Comprehensive evaluation using a large-scale bus trajectory data is conducted.

Routing and flooding are important functions in wireless networks. However, until now routing and flooding protocols are investigated separately within the same network (i.e., a WiFi network or a ZigBee network). Moreover, further performance improvement has been hampered by the assumption of the harmful cross technology interference. In this paper, we present coexistent routing and flooding (CRF), which leverages the unique feature of physical layer cross-technology communication technique for concurrently conducting routing within the WiFi network and flooding among ZigBee nodes using a single stream of WiFi packets. We extensively evaluate our design under different network settings and scenarios. The evaluation results show that CRF i) improves the throughput of WiFi network by 1.12 times than the state-of-the-art routing protocols; and ii) significantly reduces the flooding delay in ZigBee network (i.e., 31 times faster than the state-of-the-art flooding protocol).

Much recent effort has been devoted to employing Unmanned Aerial Vehicles (UAVs) to implement airport-related tasks. However, a critical issue, collision avoidance, must be fully considered in this scenario. Herein, we study the efficient UAV navigation problem considering the safety issue near an airport. In detail, we first define the safe separation between the UAV and airplanes according to related aviation regulations. Thereafter, an effective tree-based scheme for navigating the UAV has been proposed to cope with the extra uncertainties induced by keeping the safe separation. An analytical derivation of the UAV's flying time is conducted to determine the optimal battery life. Extensive simulation is conducted to verify our proposed navigation scheme and the analytical derivation.

Visual-based Unmanned Aerial Vehicles (UAVs) (e.g. equipped with an optical camera) have become more popular in daily life, because of their flexibility and convenience in capturing images/videos. Existing works mainly focus on the image/video capturing efficiency, but overlook the privacy violations that may be caused by the misuse of UAVs. In this paper, we study the Privacy Preserving Navigation (PPN) problem of the path planning of a UAV in 3D space to cover a 2D Target Area (TA), so that TA is covered while the privacy of sub-areas within TA is violated. We prove the PPN is NP-hard and propose a heuristic solution to PPN. The real word data trace driven emulation results show our solution is effective.

Routing and flooding are important functions in wireless networks. However, until now routing and flooding protocols are investigated separately within the same network (i.e., a WiFi network or a ZigBee network). Moreover, further performance improvement has been hampered by the assumption of the harmful cross technology interference. In this paper, we present coexistent routing and flooding (CRF), which leverages the unique feature of physical layer cross-technology communication technique for concurrently conducting routing within the WiFi network and flooding among ZigBee nodes using a single stream of WiFi packets. We extensively evaluate our design under different network settings and scenarios. The evaluation results show that CRF i) improves the throughput of WiFi networks by 1.2 times than the state-of-the-art routing protocols; and ii) significantly reduces the flooding delay in ZigBee networks (i.e., 31 times faster than the state-of-the-art flooding protocol).

Unmanned Aerial Vehicles (UAVs) have wide applications in many fields, e.g. multiple Unmanned Aerial Vehicles (UAVs) cooperatively tracking multiple targets. This paper studies the multiple UAVs cooperatively tracking multiple targets by vision surveillance system, where the images/videos of targets have direction requirements. One target is covered by a UAV if and only if its position is within the Field Of View (UAV) as well as the UAV is within a requested angle of the target's face direction. The objective is to maximize the total covered targets number by the UAVs, which can not be solved by existing models. A simple effective distributed, online cooperation algorithm for this problem is designed in this paper. The theoretical analysis shows our algorithm achieves constant factor to the optimal.

This paper identifies two energy saving opportunities of Wi-Fi interface emerged during smartphone's screen-off periods. Exploiting the opportunities, we propose a new power saving strategy, BackPSM, for screen-off Wi-Fi communications. BackPSM regulates client to send and receive packets in batches and coordinates multiple clients to communicate at different slots (i.e., beacon interval). The core problem in BackPSM is how to coordinate client without incurring extra traffic overheads. To handle the problem, we propose a novel paradigm, Out-of-Band Communication (OBC), for client-to-client direct communications. OBC exploits the TIM (Traffic Indication Map) field of Wi-Fi Beacon to create a free side-channel between clients. It is based upon the observation that a client may control 1 → 0 appearing on TIM bit by locally regulating packet receiving operations. We adopt this 1 → 0 as the basic signal, and leverage the time length in between two signals to encode information. We demonstrate that OBC can be used to convey coordination information with close to 100% accuracy. We have implemented and evaluated BackPSM on a testbed. The results show that BackPSM reduces screen-off energy by up to 60%, and outperforms state-of-the-art strategies by 16%-42%.

Energy balance is a critical issue in wireless sensor networks. Several mixed data transmission (MDT) schemes have been proposed to achieve energy balance. However, most existing works are lack of theoretical study, especially understanding the relationship between network-wide energy balancing and lifetime optimization. In this paper, we conduct comprehensive theoretical analysis to the two-level based MDT scheme when applying to network-wide energy balancing, and eventually to maximize the network lifetime. We propose a novel network model, named energy balance area (EBA), and formally analyze its characteristics under the two-level based MDT scheme. To maximize the network lifetime, we convert the transmission probability allocation problem in the MDT scheme into an EBA partitioning (EBA-PT) problem, which is shown to be NP-hard. We then propose a heuristic approximation algorithm to determine the optimal configuration of EBAs, which is proven in this paper to be the key for maximizing the network lifetime. In this way, we obtain a near-optimal result. Our experimental studies show that network lifetime can be further improved as compared the hop-by-hop and the two-level based MDT schemes.

The scalability trends of modern semiconductor technology lead to increasingly dense multicore chips. Unfortunately, physical limitations in area, power, off-chip bandwidth, and yield constrain single-chip designs to a relatively small number of cores, beyond which scaling becomes impractical. Multi-chip designs overcome these constraints, and can reach scales impossible to realize with conventional single-chip architectures. However, to deliver commensurate performance, multi-chip architectures require a cross-chip interconnect with bandwidth, latency, and energy consumption well beyond the reach of electrical signaling. We propose Galaxy, an architecture that enables the construction of a many-core "virtual chip" by connecting multiple smaller chiplets through optical fibers. The low optical loss of fibers allows the flexible placement of chiplets, and offers simpler packaging, power, and heat requirements. At the same time, the low latency and high bandwidth density of optical signaling maintain the tight coupling of cores, allowing the virtual chip to match the performance of a single chip that is not subject to area, power, and bandwidth limitations. Our results indicate that Galaxy attains speedup of 2.2x over the best single-chip alternatives with electrical or photonic interconnects (3.4x maximum), and 2.6x smaller energy-delay product (6.8x maximum). We show that Galaxy scales to 4K cores and attains 2.5x speedup at 6x lower laser power compared to a Macrochip with silicon waveguides.

The nanophotonic signaling technology enables efficient global communication and low-diameter networks such as crossbars that are often optically arbitrated. However, existing optical arbitration schemes incur costly overheads (e.g., waveguides, laser power, etc.) to avoid starvation caused by their inherent fixed priority, which limits their applicability in power-bounded future many-core processors. On the other hand, quality-of-service (QoS) support in the on-chip network is becoming necessary due to an increase in the number of components in the network. Most prior work on QoS in on-chip networks has focused on conventional multi-hop electrical networks, where the efficiency of QoS is hindered by the limited capabilities of electrical global communication. In this work, we exploit the benefits of nanophotonics to build a lightweight optical arbitration scheme, FeatherWeight, with QoS support. Leveraging the efficient global communication, we devise a feedback-controlled, adaptive source throttling scheme to asymptotically approach weighted max-min fairness among all the nodes on the chip. By re-using existing datapath components to exchange minimal global information, FeatherWeight provides freedom from starvation while resulting in negligible (< 1%) throughput loss compared to the best-effort baseline optical arbitration. In addition, FeatherWeight provides strong fairness, performance isolation, and differentiated service for a wide range of traffic patterns. Compared to state-of-art optical arbitration schemes, FeatherWeight reduces power consumption by up to 87% while reducing execution time by 7.5%, on average, across SPLASH-2 and MineBench traces, and improving throughput on synthetic traffic patterns by up to 17%.

Variability in device characteristics, i.e., parametric variations, is an important problem for shrinking process technologies. They manifest themselves as variations in performance, power consumption, and reduction in reliability in the manufactured chips as well as low yield levels. Their implications on performance and yield are particularly profound on 3D architectures: a defect on even a single layer can render the entire stack useless. In this paper, we show that instead of causing increased yield losses, we can actually exploit 3D technology to reduce yield losses by intelligently devising the architectures. We take advantage of the layer-to-layer variations to reduce yield losses by splitting critical components among multiple layers. Our results indicate that our proposed method achieves a 30.6% lower yield loss rate compared to the same pipeline implemented on a 2D architecture.

On-chip network is becoming critical to the scalability of future many-core architectures. Recently, nanophotonics has been proposed for on-chip networks because of its low latency and high bandwidth. However, nanophotonics has relatively high static power consumption, which can lead to inefficient architectures. In this work, we propose FlexiShare - a nanophotonic crossbar architecture that minimizes static power consumption by fully sharing a reduced number of channels across the network. To enable efficient global sharing, we decouple the allocation of the channels and the buffers, and introduce novel photonic token-stream mechanism for channel arbitration and credit distribution The flexibility of FlexiShare introduces additional router complexity and electrical power consumption. However, with the reduced number of optical channels, the overall power consumption is reduced without loss in performance. Our evaluation shows that the proposed token-stream arbitration applied to a conventional crossbar design improves network throughput by 5.5× under permutation traffic. In addition, FlexiShare achieves similar performance as a token-stream arbitrated conventional crossbar using only half the amount of channels under balanced, distributed traffic. With the extracted trace traffic from MineBench and SPLASH-2, FlexiShare can further reduce the amount of channels by up to 87.5%, while still providing better performance - resulting in up to 72% reduction in power consumption compared to the best alternative.

Future many-core processors will require high-performance yet energy-efficient on-chip networks to provide a communication substrate for the increasing number of cores. Recent advances in silicon nanophotonics create new opportunities for on-chip networks. To efficiently exploit the benefits of nanophotonics, we propose Firefly - a hybrid, hierarchical network architecture. Firefly consists of clusters of nodes that are connected using conventional, electrical signaling while the inter-cluster communication is done using nanophotonics - exploiting the benefits of electrical signaling for short, local communication while nanophotonics is used only for global communication to realize an efficient on-chip network. Crossbar architecture is used for inter-cluster communication. However, to avoid global arbitration, the crossbar is partitioned into multiple, logical crossbars and their arbitration is localized. Our evaluations show that Firefly improves the performance by up to 57% compared to an all-electrical concentrated mesh (CMESH) topology on adversarial traffic patterns and up to 54% compared to an all-optical crossbar (OP XBAR) on traffic patterns with locality. If the energy-delay-product is compared, Firefly improves the efficiency of the on-chip network by up to 51% and 38% compared to CMESH and OP XBAR, respectively.

Sharing on-chip network resources efficiently is critical in the design of a cost-efficient network on-chip (NoC). Concentration has been proposed for on-chip networks but the trade-off in concentration implementation and performance has not been well understood. In this paper, we describe cost-efficient implementations of concentration and show how external concentration provides a significant reduction in complexity (47% and 36% reduction in area and energy, respectively) compared to previous assumed integrated (high-radix) concentration while degrading overall performance by only 10%. Hybrid implementations of concentration is also presented which provide additional tradeoff between complexity and performance. To further reduce the cost of NoC, we describe how channel slicing can be used together with concentration. We propose virtual concentration which further reduces the complexity - saving area and energy by 69% and 32% compared to baseline mesh and 88% and 35% over baseline concentrated mesh.