Papers Collaborative Inference
“Collaborative Inference” 태그가 달린 논문 68편 · 필터 해제
Adaptive Termination for Multi-round Parallel Reasoning: An Universal Semantic Entropy-Guided Framework
Recent advances in large language models (LLMs) have accelerated progress toward artificial general intelligence, with inference-time scaling emerging as a key technique. Contemporary approaches leverage either sequentia…
Collaborative InferenceSmaller, Smarter, Closer: The Edge of Collaborative Generative AI
The rapid adoption of generative AI (GenAI), particularly Large Language Models (LLMs), has exposed critical limitations of cloud-centric deployments, including latency, cost, and privacy concerns. Meanwhile, Small Langu…
Collaborative InferenceA Wireless Collaborated Inference Acceleration Framework for Plant Disease Recognition
Plant disease is a critical factor affecting agricultural production. Traditional manual recognition methods face significant drawbacks, including low accuracy, high costs, and inefficiency. Deep learning techniques have…
Collaborative InferenceDeep Reinforcement LearningTowards Intelligent Edge Sensing for ISCC Network: Joint Multi-Tier DNN Partitioning and Beamforming Design
The combination of Integrated Sensing and Communication (ISAC) and Mobile Edge Computing (MEC) enables devices to simultaneously sense the environment and offload data to the base stations (BS) for intelligent processing…
Collaborative InferenceEdge-computingIntegrated sensing and communicationISACJupiter: Fast and Resource-Efficient Collaborative Inference of Generative LLMs on Edge Devices
Generative large language models (LLMs) have garnered significant attention due to their exceptional capabilities in various AI tasks. Traditionally deployed in cloud datacenters, LLMs are now increasingly moving towards…
Collaborative InferenceEdge-computingSpeculative End-Turn Detector for Efficient Speech Chatbot Assistant
Spoken dialogue systems powered by large language models have demonstrated remarkable abilities in understanding human speech and generating appropriate spoken responses. However, these systems struggle with end-turn det…
ChatbotCollaborative InferenceSpoken Dialogue Systemstext-to-speech+1G-Boost: Boosting Private SLMs with General LLMs
Due to the limited computational resources, most Large Language Models (LLMs) developers can only fine-tune Small Language Models (SLMs) on their own data. These private SLMs typically have limited effectiveness. To boos…
Collaborative InferenceTheoretical Insights in Model Inversion Robustness and Conditional Entropy Maximization for Collaborative Inference Systems
By locally encoding raw data into intermediate features, collaborative inference enables end users to leverage powerful deep learning models without exposure of sensitive raw data to cloud servers. However, recent studie…
Collaborative InferenceRepresentation LearningPhyloVAE: Unsupervised Learning of Phylogenetic Trees via Variational Autoencoders
Learning informative representations of phylogenetic tree structures is essential for analyzing evolutionary relationships. Classical distance-based methods have been widely used to project phylogenetic trees into Euclid…
Collaborative InferenceRepresentation LearningDistrEE: Distributed Early Exit of Deep Neural Network Inference on Edge Devices
Distributed DNN inference is becoming increasingly important as the demand for intelligent services at the network edge grows. By leveraging the power of distributed computing, edge devices can perform complicated and re…
Autonomous VehiclesCollaborative InferenceDistributed ComputingCITER: Collaborative Inference for Efficient Large Language Model Decoding with Token-Level Routing
Large language models have achieved remarkable success in various tasks but suffer from high computational costs during inference, limiting their deployment in resource-constrained applications. To address this issue, we…
Collaborative InferenceLanguage ModelingLanguage ModellingLarge Language ModelMoE$^2$: Optimizing Collaborative Inference for Edge Large Language Models
Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of natural language processing tasks. Exploiting the heterogeneous capabilities of edge LLMs is crucial for diverse emerging appl…
Collaborative InferenceCollaboration of Large Language Models and Small Recommendation Models for Device-Cloud Recommendation
Large Language Models (LLMs) for Recommendation (LLM4Rec) is a promising research direction that has demonstrated exceptional performance in this field. However, its inability to capture real-time user preferences greatl…
Collaborative InferenceDevice-Cloud CollaborationEdge-computingRerankingDistributed Mixture-of-Agents for Edge Inference with Large Language Models
Mixture-of-Agents (MoA) has recently been proposed as a method to enhance performance of large language models (LLMs), enabling multiple individual LLMs to work together for collaborative inference. This collaborative ap…
Collaborative InferenceDistributed ComputingGREAT: Geometry-Intention Collaborative Inference for Open-Vocabulary 3D Object Affordance Grounding
Open-Vocabulary 3D object affordance grounding aims to anticipate ``action possibilities'' regions on 3D objects with arbitrary instructions, which is crucial for robots to generically perceive real scenarios and respond…
Collaborative InferenceObjectDistributed Collaborative Inference System in Next-Generation Networks and Communication
With the rapid advancement of artificial intelligence, generative artificial intelligence (GAI) has taken a leading role in transforming data processing methods. However, the high computational demands of GAI present cha…
Collaborative InferenceCE-CoLLM: Efficient and Adaptive Large Language Models Through Cloud-Edge Collaboration
Large Language Models (LLMs) exhibit remarkable human-like predictive capabilities. However, it is challenging to deploy LLMs to provide efficient and adaptive inference services at the edge. This paper proposes a novel …
Collaborative InferenceLarge Language ModelCollaborative Inference over Wireless Channels with Feature Differential Privacy
Collaborative inference among multiple wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications, particularly for sensing and computer vision. This approach typically inv…
Collaborative InferencePrivacy PreservingSplitLLM: Collaborative Inference of LLMs for Model Placement and Throughput Optimization
Large language models (LLMs) have been a disruptive innovation in recent years, and they play a crucial role in our daily lives due to their ability to understand and generate human-like text. Their capabilities include …
Collaborative InferenceInformation RetrievalNatural Language UnderstandingEdge-device Collaborative Computing for Multi-view Classification
Motivated by the proliferation of Internet-of-Thing (IoT) devices and the rapid advances in the field of deep learning, there is a growing interest in pushing deep learning computations, conventionally handled by the clo…
ClassificationCollaborative InferenceDeep Learning