Enterprise AI Analysis

DNN Partitioning for Cooperative Inference in Edge Intelligence: Modeling, Solutions, Toolchains

With rapid advancements in artificial intelligence and Internet of Things technologies, the deployment of deep neural network (DNN) models on the edge nodes and the end nodes has become an essential trend. However, the limited computational power, storage capacity, and resource constraints of these devices present significant challenges for deep learning inference. Traditional acceleration methods, such as model compression and hardware optimization, often struggle to balance real-time performance, accuracy, and cost-effectiveness. To address these challenges, collaborative inference through DNN partitioning has emerged as a promising solution. This article provides a comprehensive overview of architectural frameworks for DNN partitioning in collaborative inference. We establish a unified mathematical framework to describe various architectures, DNN models, and their associated optimization problems. In addition, we systematically classify and ana-lyze existing partitioning strategies based on partition count and granularity. Furthermore, we summarize commonly used experimental setups and tools, offering practical insight into implementation. Finally, we discuss key challenges and open issues in DNN partitioning for collaborative inference, such as ensuring data security and privacy and efficiently partitioning large-scale models, providing valuable guidance for future research.

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Executive Impact & Key Findings

This article presents a comprehensive survey of DNN partitioning for collaborative inference, addressing the challenges of deploying deep models on resource-constrained edge and end nodes. It unifies diverse collaborative architectures, DNN structures, and optimization objectives into a modeling framework and systematically compares partitioning strategies. Based on this analysis, several important directions for future research are highlighted. Ensuring data security and privacy at partition points, developing robust and adaptive partitioning strategies for dynamic and heterogeneous environments, and efficiently handling large-scale models are critical areas that require further exploration. In addition, lightweight and scalable solutions that balance latency, energy consumption, and monetary cost remain underexplored. We hope this work provides a theoretical foundation and practical reference to support further research and real-world deployment of collaborative DNN inference systems.

140 Total Downloads

Feb 4, 2026 Published Date

Nov 7, 2025 Accepted Date

Deep Analysis & Enterprise Applications

Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.

Collaborative Inference Architectures

Diverse architectural paradigms in edge computing define unique challenges and opportunities for DNN partitioning.

In this architecture, the end device performs initial computations and offloads remaining layers to an edge server. Key issues include early exit and model compression strategies.

Chain-based DNNs: Neurosurgeon [59] balances computation and communication costs.
DAG-based DNNs: DADS [63] uses graph cut for complex inter-layer dependencies.
Transformer LLMs: [53] explores dynamic partitioning under variable wireless conditions.
Model Optimization Integration: Works like [60, 61] integrate early exit mechanisms, while [67, 68] incorporate model compression to enhance efficiency.

Reference	DNN Structure	Key Issues in DNN Partitioning
[59-62]	Chain	Early exit, Model compression
[63-68]	DAG	Model compression
[53]	Transformer	-

Multiple end devices offload tasks to a shared edge server, which acts as a centralized coordinator. Resource allocation and task offloading are critical challenges.

Resource Allocation: Jointly optimized with DNN partitioning [33, 69-72, 76] or decoupled [73, 75, 81, 83] to manage shared edge nodes.
Task Offloading: Optimizing partition points based on task queue and communication conditions [77-80] to prevent resource contention and queue delays.

Reference	Application Scenario	Key Issues Considered in DNN Partitioning
[33, 69-77]	IoT, Edge Computing	Resource allocation
[78-82]	Edge Computing	Task Offloading

A single end device offloads tasks to multiple edge servers for distributed execution. This introduces challenges in dynamic task offloading, mobility, and reliability.

Task Offloading: Dynamic assignment considering computational load, network conditions, and resource availability [85].
Mobility: Fluctuating network conditions and device movement require frequent task migration and adaptation [85-88].
Reliability: Robustness is ensured via overlapping DNN partitions and redundancy to handle disconnections and uneven resource distribution [87, 89].

Reference	Application Scenario	Key Issues Considered in DNN Partitioning
[84, 85]	Edge Computing	Task offloading
[85-88]	Mobile Edge Computing	Mobility-induced task offloading
[87, 89]	Vehicular Networks (V2I, V2V)	Reliability

Multiple end devices share access to multiple edge servers with overlapping service areas. This creates complex challenges in task offloading, resource allocation, and mobility.

Decoupled Optimization: Many studies separate partitioning, resource allocation, and offloading subproblems to reduce complexity [74, 90, 91].
Integrated Approaches: [47] jointly optimizes all components in dynamic vehicular edge environments for long-term inference performance.

Reference	Application Scenario	Key Issues Considered in DNN Partitioning
[90-92]	IoT, Edge Camera Network	Task Offloading, Resource Allocation
[47]	VEC	Mobility-induced Task Offloading, Resource Allocation

Decentralized architecture where devices autonomously execute dynamically partitioned DNN segments. Heterogeneity, reliability, and task offloading are primary concerns.

Heterogeneity: Adaptive, fine-grained partitioning mechanisms [57, 95] and joint device selection/model partitioning [50-52] handle diverse device capabilities.
Reliability: Robust task replication, failure recovery, and fault-tolerant scheduling [96] are essential for dynamic P2P systems.
Task Offloading: Managing multiple DNN inference tasks, from chain-structured [97] to DAG-structured [98] models, to avoid resource contention.

Reference	Application Scenario	Key Issues Considered in DNN Partitioning
[32, 50-52, 57, 66, 93, 95, 99-104]	IoT, Edge Intelligence	Heterogeneity
[96]	UAV Swarm	Reliability
[97, 98]	Fog and Edge Computing	Task offloading
[94]	Vehicular Networks (V2V)	Mobility-induced task offloading

Key Optimization Metrics for Collaborative Inference

Evaluating Quality of Service (QoS) for DNN partitioning focuses on crucial metrics, each with specific modeling approaches.

Total execution latency (L) is the sum of computation (Lc), data transmission (Lt), and queue delay (Lque).

Modeling:

Lc: Computed based on computational power (com_i) and GFLOPs of partitions.
Lt: Estimated using bandwidth (B_ij) and data volume (Data_k). Some studies refine this with Shannon-Hartley theorem for SNR [65, 67, 71, 72, 80, 83, 84, 92, 96].
Lque: Analyzed using queue theory models (M/M/1, M/D/1) for tasks waiting at resource-constrained nodes [81].

Example: For a MobileNetV2 partitioned between smartphone and edge server, computation latency is 2.4 ms (Node 1) + 0.36 ms (Node 2). Transmission latency for 2.5 MB data is 1 s (2.5MB * 8 bits/MB / 20 Mbps). Queueing delay (M/M/1, arrival 5/s, service 10/s) is 0.2 s. Total end-to-end latency: ~1.203 s.

Total energy consumption (E) combines computational (Ec) and transmission (Et) energy.

Modeling:

Ec: Product of computational latency and node-specific energy consumption rate (a_i). Nonlinear models (a_i × com³ for CPU/GPU) are used in [33, 72, 78].
Et: Product of transmission power (β_n) and transmission time. Refined by Shannon-Hartley theorem for effective data rates [57, 78].

Example: Smartphone computational energy: 2W * 0.0024s = 4.8 mJ. Transmission energy: 1.5W * 1s = 1.5 J. Total energy: ~1.5048 J.

Total cost (C) comprises computation (Cc) and data transmission (Ct) costs.

Modeling:

Cc: Product of execution latency and unit operational cost (γ_i) [107-109]. Some model γ_i as a function of computational capacity [75].
Ct: Product of transmission time and unit cost of communication channel utilization (δ_nk) [108].

Example: Electricity price $0.1/kWh. Computation cost: $1.33 × 10^-10. Transmission cost: $4.17 × 10^-8. Total monetary cost: ~$4.18 × 10^-8 per inference.

DNN inference accuracy (A) depends on input data size, DNN model structure, and early exit branch points (F(Data₀, G, Exit_m)).

Modeling:

Empirical analysis: Direct measurement on standard public datasets [71].
Predictive modeling: Training models to estimate accuracy [59, 60] and expected accuracy of early-exit branches [61, 62, 106].

System reliability (R) considers both computational node failure probability (φ_nk) and transmission link failure probability (ψ_nk,nk+1).

Modeling: R = Π(1 – φ_nk) · Π(1 – ψ_nk,nk+1) · (1 – ψ_np,n1) [87, 89, 96]. This accounts for failures in computation, forward transmission, and the return link for final results.

Case Study: MobileNetV2 Partitioning Example

Consider a MobileNetV2 model (approx. 300 MFLOPs) partitioned between a smartphone (Node 1) and an edge server (Node 2). Layers 1-5 (120 MFLOPs) run on Node 1, and layers 6-10 (180 MFLOPs) are offloaded to Node 2. The output of layer 5 (2.5 MB feature map) is transmitted to the server. Smartphone (50 GFLOPS), Server (500 GFLOPS), uplink bandwidth (20 Mbps). This example illustrates how the analytical models for latency, energy, and cost are applied to derive concrete performance estimates for a collaborative DNN inference task.

93% Reduction in data transmission volume for Tiny YOLOv2 max5 layer output, minimizing bandwidth requirements.

Comparison with Related Works on DNN Partitioning

Literature	Collaborative Inference Architecture	System Modeling & Optimization Problem	DNN Model Issues & Solutions	Transformer Partitioning Included	Collaborative Architecture Related Issues & Solutions	Experimental Tools & Datasets Summary
[34]	Cloud&Edge&End	✓	✓	X	✓	X
[35]	Device & Server	X	✓	X	X	X
[36]		X	✓	X	X	X
[37]	Cloud&Edge&End	✓	✓	X	✓	✓
[38]	Device-server	X	✓	X	✓	X
[39]		✓	✓	X	✓	X
[40]	Cloud&Edge&End	✓	✓	X	✓	✓
[41]	Cloud&Edge&End	✓	✓	X	✓	✓
[42]	Edge&End	X	✓	X	X	X
[43]	Device & Server	✓	✓	X	✓	X
[44]		X	✓	X	X	X
[45]	Cloud&Edge&End	✓	✓	✓	✓	✓
[This paper]	Cloud&Edge&End	✓	✓	✓	✓	✓

DNN Partitioning Strategies and Solution Spaces

A classification of DNN partitioning methods based on solution space dimensionality and integration with other optimization techniques.

Enterprise Process Flow

Optimization Problem (Latency, Energy, Cost, Accuracy, Reliability)

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Solution Space (One-dimensional, Two-dimensional, Multi-dimensional)

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Partition (DNN layers, DNN sub-layers)

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Model Optimization Decisions

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Resource Allocation Decisions

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Task Offloading Decisions

This chart illustrates the interconnected nature of DNN partitioning within collaborative inference, showing how optimization objectives guide the exploration of solution spaces, which in turn inform decisions about partitioning granularity, model optimization, resource allocation, and task offloading.

Focuses on dividing DNNs at single layer boundaries, primarily for one-to-one end-edge architectures.

Linear Search: Evaluates candidate partition points based on latency/energy to find optimal splits [59, 66, 72, 76, 79].
Graph-based Cut: Transforms partitioning into a minimum cut problem for DAG-based DNNs, balancing computation and communication costs [63, 65, 67, 111].
DRL Method: Adapts to dynamic network conditions by continuously learning optimal partition points [53].

Reference	Collaborative Architecture	Targeted Problem	Optimization Objective	Constraints	Method
[59, 72, 79]	One-to-One	Chain-structured DNN	Latency, Energy	C1, C3	Linear Search
[66, 77, 85, 86]	One-to-One	DAG-structured DNN	Latency	C1, C3	Linear Search
[63, 65, 67, 111]	One-to-One	DAG-structured DNN	Latency	C1, C3	Graph-based Cut
[53]	One-to-One	Transformer	Latency, Accuracy	C1	DRL

Integrates DNN partitioning with model-level optimizations like early exit mechanisms and model compression to enhance efficiency, particularly for one-to-one architectures.

Partitioning + Early Exit: Combines optimal partition points with early exit branches to reduce latency and cost. Strategies include offline configuration tables [60] or confidence-based early exits [61, 62].
Partitioning + Model Compression: Co-optimizes by selecting partition points with smaller output feature dimensions and applying sparsity-aware pruning to edge-deployed submodels [68].

Reference	Collaborative Architecture	Targeted Problem	Optimization Objective	Constraints	Method
[60]	One-to-One	Early Exit	Accuracy	C1, C3	Offline Configuration
[61]	One-to-One	Early Exit	Latency	C1, C5	Confidence Estimation
[62]	One-to-One	Early Exit	Latency, Accuracy	C1, C5	ILP Optimizer
[68]	One-to-One	Model Compression	Latency, Accuracy	C1, C5	Decoupled Optimization

Addresses DNN partitioning and resource allocation, mainly in one-to-multiple architectures, either through decoupled or joint optimization.

Decoupled Optimization: Separates partitioning decisions from resource allocation. Examples include offline configuration tables with auction mechanisms [73] or minimum-cut algorithms followed by game theory [75, 81, 83].
Joint Optimization: Treats partitioning and resource allocation as a unified problem. Approaches include Iterative Alternating Optimization (IAO) [69], Deep Reinforcement Learning (DRL) [33, 113], and game-theoretic models [72, 76].

Reference	Collaborative Architecture	Targeted Problem	Optimization Objective	Constraints	Method
[73, 74]	One-to-Multiple	Resource Allocation	Energy	C1, C2	Decoupled Optimization
[75]	One-to-Multiple	Resource Allocation	Latency, Energy	C1, C2	Decoupled Optimization
[81]	One-to-Multiple	Resource Allocation	Latency	C1, C2, C3	Iterative Alternating
[83]	One-to-Multiple	Resource Allocation	Latency	C1, C2, C3	Iterative Alternating
[69]	One-to-Multiple	Resource Allocation	Latency	C1, C2	Iterative Alternating
[33]	One-to-Multiple	Resource Allocation	Energy	C1, C2	DRL
[113]	One-to-Multiple	Resource Allocation	Cost	C1, C2, C3	DRL
[72, 76]	One-to-Multiple	Resource Allocation	Latency, Energy	C1, C2	Game Theory

For computationally intensive DNNs, multi-partitioning across multiple nodes offers greater flexibility, especially in scenarios with heterogeneous nodes, dynamic edge availability, and reliability demands. These methods integrate partitioning with offloading decisions.

Decoupled Optimization: Treats partitioning and offloading as separate problems to reduce complexity. Examples include evaluating latency/cost tradeoffs to select partition points [109], iterative multi-partitioning with genetic algorithms [93], or heuristic search with graph representations [88]. Replicated partitioning strategies enhance reliability [87, 89].
Joint Optimization: Addresses partitioning and offloading as a unified problem. This includes layer-wise sequential decision-making [97, 85], topological sorting for DAG-structured DNNs [98], and learning-based methods using DRL [78, 80, 108] to adapt to dynamic conditions.

Reference	Collaborative Architecture	Targeted Problem	Optimization Objective	Constraints	Method
[84, 109]	One-to-Multiple	Task Offloading	Latency, Cost	C1	Decoupled Optimization
[86, 88]	One-to-Multiple	Mobility	Latency	C1, C3	Decoupled Optimization
[87, 89]	One-to-Multiple	Reliability	Latency, Reliability	C1	Decoupled Optimization
[93]	Peer-to-Peer	Task Offloading	Latency	C1, C4	Algorithm-Based Method
[85]	One-to-Multiple	Mobility	Latency	C1, C3	Algorithm-Based Method
[97, 98, 114]	Peer-to-Peer	Task Offloading	Latency	C1	Heuristic Method
[115]	One-to-Multiple	Task Offloading	Latency	C1, C3	Heuristic Method
[99, 107]	Peer-to-Peer	Task Offloading	Latency, Energy	C1, C2, C6	Heuristic Method
[78, 80, 108]	One-to-Multiple	Mobility	Latency, Energy, Cost	C1	Learning-Based Method
[94]	Peer-to-Peer	Mobility	Latency, Energy	C1, C2, C3, C6	Learning-Based Method

These approaches integrate partitioning granularity, resource allocation, task offloading, and model optimization to address complex challenges in multi-user, dynamic environments.

Joint Management (Partitioning, Model Optimization, Resource Allocation): Common in multiple-to-one architectures, often uses DRL for adaptive policy learning. Examples include MAMO framework [70] and DRL for joint partitioning, early exit, and resource distribution [106, 71].
Joint Management (Partitioning, Model Optimization, Task Offloading): DT-assisted methods evaluate offloading decisions for DNN inference tasks, enabling dynamic early exits from local inference or offloading to edge servers [82].
Joint Management (Partitioning, Task Offloading, Resource Allocation): Common in multi-to-multi architectures. May use decoupled optimization [90, 91, 92] or tightly coupled joint optimization with DRL [47] to capture interdependencies.

References	Architecture	Problem Scope	Optimization Objective	Constraints	Approach
[70, 71, 106]	Multiple-to-One	Joint management (1)	Latency	C1, C2, C5	DRL
[82]	Multiple-to-One	Joint management (2)	Latency, energy, accuracy	C1	DT-assisted DRL
[90, 91]	Multiple-to-Multiple	Joint management (3)	Latency, energy	C1, C2, C4, C6	Decoupled optimization
[92]	Multiple-to-Multiple	Joint management (3)	Latency	C1, C2	Partially decoupled
[47]	Multiple-to-Multiple	Joint management (3)	Latency	C1, C2	Joint optimization

Focuses on fine-grained inference parallelization within DNNs by dividing layers into smaller computational units, suitable for heterogeneous edge environments.

Convolutional Layers: Partitioning feature maps into segments for parallel processing. DeepThings [119] uses Fusion Tile Partitioning for overlapping computations and reduced data transfer. D3 [116] introduces Vertical Separation Module (VSM) for accuracy. CoEdge [57] addresses padding issues for large kernels.
Common DNN Layers: Extends sub-layer partitioning by rearranging neurons to minimize interdependencies and communication overhead [100, 121].
Transformer Models: Addresses multi-head self-attention and MLP blocks. Block Parallelism (BP) [123] partitions weight matrices row-wise/column-wise to decouple layers and defer communication. Hepti [58] dynamically offloads GEMM operations, switching between Weight Stationary (WS), 1D tiled WS, and 2D tiled WS strategies based on auxiliary memory.

Reference	Collaboration Architecture	Targeted Problem	Optimization Objective	Constraints	Approach
[116]	One-to-Multiple	Convolutional Layers Parallel	Latency	C1	VSM
[117, 118]	One-to-One	Convolutional Layers Parallel	Latency	C1	Greedy Algorithm
[119]	Peer-to-Peer	Convolutional Layers Parallel	Latency	C1	FTP
[120]	One-to-Multiple	Convolutional Layers Parallel	Energy	C1, C6	AOFL
[57]	Peer-to-Peer	Convolutional Layers Parallel	Latency	C1	Only Neighbor
[112]	One-to-Multiple	Common DNN Layers Parallel	Latency	C1	Critical Path Method
[100, 121]	Peer-to-Peer	Common DNN Layers Parallel	Latency	C1	Rearrangement of Neurons
[122]	Peer-to-Peer	Transformer Parallel	Latency	C1	Position-wise partitioning
[123]	Peer-to-Peer	Transformer Parallel	Latency	C1	hybrid row- and column-wise
[58]	Peer-to-Peer	Transformer Parallel	Latency	C1	WS & 1D & 2D

Experimental Setup and Tools

An overview of commonly used DNN models, frameworks, datasets, computing nodes, and communication/resource control tools in collaborative inference research.

DNN Models: Categorized into Chain-based (AlexNet, VGG, MobileNet), DAG-based (ResNet, GoogleNet), and Transformer-based (BERT, GPT-2, LLaMA2) architectures.

Frameworks: Essential for training, optimization, and evaluation. Includes PyTorch, TensorFlow, Caffe, BranchyNet, Chainer.

Category	Tool or Dataset	URL
Framework	PyTorch	https://pytorch.org/
	TensorFlow	https://www.tensorflow.org/
	Caffe	https://caffe.berkeleyvision.org/
	BranchyNet	https://github.com/mit-han-lab/branchynet

Standardized benchmarks for model performance and partitioning strategies. Categorized by task type:

Image Classification: CIFAR, Caltech-256, ImageNet, ILSVRC2012, SeaShip.
Video: BDD 100K, UCF-101.
Text Classification: AG News, GLUE, WikiText-2.

Category	Tool or Dataset	URL
Image Classification Datasets	CIFAR	https://www.cs.toronto.edu/~kriz/cifar.html
	Caltech-256	http://www.vision.caltech.edu/Image_Datasets/Caltech256/
	ImageNet	http://www.image-net.org/
	ILSVRC2012	http://www.image-net.org/challenges/LSVRC/2012/
Video Datasets	SeaShip	https://github.com/seaship-dataset/seaship
	BDD 100K	https://bdd-data.berkeley.edu/
	UCF-101	https://www.crcv.ucf.edu/data/UCF101.php
Text Classification Datasets	AG News	https://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html
	GLUE	https://gluebenchmark.com/
	WikiText-2	https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/

Computing Nodes:

Server: High-performance CPUs (Intel Xeon E5-2620 v4, i7-8700/9700K, i3-3240) and GPUs (NVIDIA Titan V100, RTX 2080 Ti, Quadro K620).
Device: Edge and embedded devices (Raspberry Pi Series 3B/3B+/4B/4 Model B, NVIDIA Jetson Nano/Xavier NX).

Network Communication Tools: Essential for data exchange. Includes WiFi, Ethernet/LAN, ZeroMQ (message queue), gRPC (RPC protocol).

Category	Node or Tool	URL
Server	Intel Xeon E5-2620 v4 / Intel i7-8700 / Intel i7-9700K / Intel i3-3240	—
	NVIDIA Titan V100 / RTX 2080 Ti / Quadro K620	—
Device	Raspberry Pi 3B / 3B+ / 4B / Raspberry Pi 4	—
	Model B	—
	NVIDIA Jetson Nano / Xavier NX	—
Network Communication	WiFi	—
	Ethernet / LAN	—
	ZeroMQ	https://zeromq.org/
	gRPC	https://grpc.io/

Resource Control Tools: Simulate network dynamics and evaluate DNN inference performance. Includes WonderShaper, COMCAST, Linux Traffic Control (tc), Sleep Operation, Docker, stress-ng.

Parameter Analysis & Measurement Tools: Evaluate inference performance, computational efficiency, model complexity. Includes TensorFlow Benchmarking Tool, PALEO, LINPACK, thop, Torchstat, NetScope.

Category	Tool or Dataset	URL
Bandwidth	WonderShaper	https://github.com/magnific0/wondershaper
	COMCAST	https://github.com/ANRGUSC/COMCAST
	Linux Traffic Control (tc)	https://man7.org/linux/man-pages/man8/tc.8.html
	Sleep Operation	https://man7.org/linux/man-pages/man1/sleep.1.html
	Belgium 4G/LTE Bandwidth Logs Dataset	https://github.com/ANRGUSC/COMCAST/tree/master/real-traces/belgium
Resource	Docker	https://www.docker.com/
Memory	stress-ng	https://manpages.ubuntu.com/manpages/focal/man1/stress-ng.1.html
Analysis Tool	TensorFlow Benchmarking Tool	https://www.tensorflow.org/guide/benchmarking
	PALEO	https://github.com/cucapra/paleo
	LINPACK	http://www.netlib.org/benchmark/linpackds/
Measurement Tool	thop	https://github.com/Lyken17/pytorch-OpCounter
	Torchstat	https://github.com/Swall0w/torchstat
	NetScope	https://netron.app/

Research Challenges and Open Issues

Key challenges identified for advancing collaborative DNN inference systems.

DNN partitioning introduces risks in heterogeneous, untrusted environments, including model inversion attacks, adversarial perturbations, and man-in-the-middle (MITM) attacks on intermediate activations.

Mitigation: Lightweight mechanisms like device authentication, trusted execution environments (TEEs), tamper-proof storage, blockchain for verifiable node behavior, and differential privacy [141, 142].
Future Focus: Adaptive mechanisms that balance privacy and efficiency under resource constraints, as current methods (homomorphic encryption [143], secure multiparty computation [144]) incur high overhead.

Ensuring task completion amid uncertainties (node failures, system changes, communication environment fluctuations) is crucial. Most existing works rely on multi-replica strategies, which can be resource-intensive.

Challenge: Designing fault-tolerant systems that quickly migrate or reallocate tasks upon node failures, maintaining inference continuity [146].
Future Focus: Dynamic re-partitioning and automated scheduling coordination across nodes [147] in response to overload or failure for uninterrupted inference services.

Large-scale models (Transformers, GPT series) pose challenges due to deep architectures, massive parameters, multi-head self-attention, feedforward layers, inter-layer dependencies, uneven computation, and high memory usage.

Challenge: Communication overhead from large intermediate data (e.g., attention maps) can offset distributed execution benefits.
Future Focus: Resource-aware, hardware-adaptive partitioning strategies, runtime profiling, pipeline scheduling, and compression techniques to reduce communication costs while maintaining accuracy in heterogeneous collaborative environments.

Calculate Your Potential AI ROI

Estimate the direct financial and productivity impact of implementing advanced DNN partitioning strategies in your enterprise.

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Your AI Transformation Roadmap

A typical phased approach to implementing advanced DNN partitioning for optimized edge intelligence.

Phase 1: Discovery & Assessment

Comprehensive evaluation of existing DNN models, edge infrastructure, and specific performance bottlenecks. Define clear objectives and success metrics for collaborative inference.

Phase 2: Architecture & Partitioning Design

Select optimal collaborative architectures (e.g., one-to-one, multi-to-one) and partitioning strategies (layer-wise, sub-layer, tensor-level) based on identified constraints and objectives (latency, energy, cost, accuracy, reliability).

Phase 3: Prototype & Validation

Develop a proof-of-concept using selected DNN models and partitioning toolchains. Rigorous testing in a simulated edge environment to validate performance, accuracy, and reliability against benchmarks.

Phase 4: Deployment & Optimization

Gradual rollout to production environments, leveraging dynamic adaptation techniques and real-time monitoring. Continuous optimization through DRL or heuristic-based adjustments to handle runtime variability.

Phase 5: Scaling & Future-Proofing

Expand deployment across wider enterprise ecosystems. Integrate advanced security protocols and prepare for future large-scale models and evolving edge intelligence demands.

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Enterprise AI Analysis

DNN Partitioning for Cooperative Inference in Edge Intelligence: Modeling, Solutions, Toolchains

Executive Impact & Key Findings

Deep Analysis & Enterprise Applications

Collaborative Inference Architectures

Key Optimization Metrics for Collaborative Inference

Case Study: MobileNetV2 Partitioning Example

Comparison with Related Works on DNN Partitioning

DNN Partitioning Strategies and Solution Spaces

Enterprise Process Flow

Experimental Setup and Tools

Research Challenges and Open Issues

Calculate Your Potential AI ROI

Your AI Transformation Roadmap

Phase 1: Discovery & Assessment

Phase 2: Architecture & Partitioning Design

Phase 3: Prototype & Validation

Phase 4: Deployment & Optimization

Phase 5: Scaling & Future-Proofing

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