Efficient Partitioning and Allocation of Deep Neural Networks Across Embedded Devices: A Systematic Review

Core Concepts

Embedded distributed inference of Deep Neural Networks is an efficient and scalable approach for deploying machine learning models on resource-constrained devices. This systematic review analyzes techniques and methods to partition and allocate the inference of Deep Neural Networks across a network of embedded devices.

Abstract

This systematic review examines over 100 papers published in the last six years that describe techniques and methods for distributing the inference of Deep Neural Networks across embedded and edge devices.
The key insights from the review are:

Runtime Flexibility:

Static approaches partition the network offline, while adaptive approaches recalculate the distribution at runtime based on changes in the environment (e.g., bandwidth, device performance).
Adaptive approaches mainly focus on adapting to bandwidth changes, with fewer studies exploring adaptation to other variables like device battery level or task arrival rate.

Partition Granularity:

Horizontal partitioning assigns groups of consecutive layers to devices in a pipelined manner, while vertical partitioning splits the output feature maps of a layer across multiple devices in parallel.
Horizontal partitioning is more common, likely due to its simplicity. Vertical partitioning with layer fusion techniques show promise but require further research.

Optimization Metrics:

The most common metrics are latency, throughput, and energy consumption. Some studies also consider metrics like communicated data, accuracy, and privacy.
Relative comparisons are typically used to evaluate performance improvements over single-device baselines.

Device Cost Modeling:

Analytical models, simulators, and offline/online profiling are used to estimate the cost of executing layers on different devices.
Offline profiling combined with regression models is a common approach to balance accuracy and efficiency.

The review identifies several promising research directions, including exploring additional adaptation variables, developing new evaluation metrics, and optimizing for emerging concerns like privacy and energy efficiency.

Stats

"The continuously increasing memory footprint and computational complexity of current DNN architectures, together with hard constraints such as energy consumption and latency requirements, have motivated a growing interest in finding an efficient and automated distribution of the inference of an DNN across multiple devices."
"Previous surveys, such as [8, 20] have addressed the distribution of AI algorithms across multiple devices, but have focused on different aspects of the topic (federated learning, reinforcement learning, active learning, pervasive inference, privacy of distributed AI systems, etc.), thus dedicating less space to the particular problem of distributed inference."
"Contrary to previous surveys, this work focuses on multiple aspects of the techniques and methodologies used to achieve distributed inference, exploring how to partition a DNN and allocate the execution of each section across a variety of devices."

Quotes

"Embedded distributed inference of Neural Networks has emerged as a promising approach for deploying machine-learning models on resource-constrained devices in an efficient and scalable manner."
"The inference task is distributed across a network of embedded devices, with each device contributing to the overall computation by performing a portion of the workload."
"As the demand for intelligent systems and the complexity of the deployed neural network models increases, this approach is becoming more relevant in a variety of applications such as robotics, autonomous vehicles, smart cities, Industry 4.0 and smart health."

Key Insights Distilled From

Embedded Distributed Inference of Deep Neural Networks: A Systematic Review

by Fede... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.03360.pdf

Deeper Inquiries

How can the distribution algorithms be extended to handle dynamic changes in the environment, such as devices being added or removed from the system, or changes in the input data characteristics?

In order to handle dynamic changes in the environment, distribution algorithms for Deep Neural Networks (DNNs) need to be adaptive and flexible. One approach is to incorporate real-time monitoring and feedback mechanisms that can detect changes in the system, such as devices being added or removed, fluctuations in bandwidth, or variations in input data characteristics. Here are some strategies to extend distribution algorithms to handle dynamic changes:

Adaptive Partitioning: Implement algorithms that can dynamically adjust the partitioning of the DNN across devices based on changes in the system. This could involve re-evaluating the allocation of layers to devices in response to fluctuations in device capabilities or network conditions.

Dynamic Resource Allocation: Develop algorithms that can dynamically allocate resources based on the current state of the system. This could involve redistributing computational tasks based on the availability of devices or adjusting the workload distribution to optimize performance.

Feedback Mechanisms: Integrate feedback mechanisms that continuously monitor the performance of the distributed system and provide insights into potential adjustments. This feedback loop can help the algorithm adapt to changing conditions in real-time.

Predictive Modeling: Use predictive modeling techniques to anticipate changes in the environment and proactively adjust the distribution of the DNN. By forecasting potential variations in device availability or input data characteristics, the algorithm can preemptively optimize the system.

Fault Tolerance: Implement fault-tolerant mechanisms that can handle device failures or network disruptions. By incorporating redundancy and failover strategies, the system can continue to operate smoothly even in the face of unexpected changes.

By incorporating these strategies, distribution algorithms can be extended to handle dynamic changes in the environment effectively, ensuring optimal performance and adaptability in varying conditions.

How can the potential privacy and security implications of distributing the inference of a Deep Neural Network across multiple devices be addressed?

Distributing the inference of a Deep Neural Network (DNN) across multiple devices introduces potential privacy and security concerns, especially when sensitive data is being processed. To address these implications, several measures can be implemented:

Data Encryption: Utilize encryption techniques to secure data transmission between devices and ensure that sensitive information is protected from unauthorized access.

Secure Communication Protocols: Implement secure communication protocols such as SSL/TLS to establish encrypted connections and prevent data interception during inference tasks.

Privacy-Preserving Techniques: Employ privacy-preserving methods like differential privacy, homomorphic encryption, or federated learning to perform inference without exposing raw data to individual devices.

Access Control and Authentication: Implement robust access control mechanisms and authentication protocols to restrict unauthorized access to the distributed system and ensure that only authenticated users can interact with the network.

Anonymization: Apply data anonymization techniques to remove personally identifiable information from the input data, reducing the risk of privacy breaches during inference.

Regular Security Audits: Conduct regular security audits and vulnerability assessments to identify and address potential security loopholes in the distributed system.

Compliance with Regulations: Ensure compliance with data protection regulations such as GDPR, HIPAA, or CCPA to uphold privacy standards and protect user data.

Secure Device Management: Implement secure device management practices to prevent unauthorized devices from joining the network and ensure that only trusted devices are part of the distributed system.

By incorporating these privacy and security measures, the potential risks associated with distributing the inference of DNNs across multiple devices can be mitigated, safeguarding sensitive data and maintaining the integrity of the system.

How can the evaluation of distributed inference techniques be standardized to enable more meaningful comparisons between different approaches, beyond just relative performance improvements?

Standardizing the evaluation of distributed inference techniques is crucial to enable meaningful comparisons between different approaches and facilitate advancements in the field. Here are some strategies to standardize the evaluation process:

Benchmark Datasets: Establish benchmark datasets that are commonly used to evaluate distributed inference techniques. By using standardized datasets, researchers can compare the performance of their algorithms on a consistent basis.

Performance Metrics: Define a set of standardized performance metrics, such as latency, throughput, energy efficiency, and accuracy, that should be reported in research papers evaluating distributed inference techniques. This will allow for direct comparisons between different approaches.

Evaluation Frameworks: Develop evaluation frameworks or toolkits that researchers can use to assess the performance of their distributed inference algorithms in a consistent manner. These frameworks can provide guidelines on experimental setup, metrics to report, and data analysis procedures.

Open Access to Code and Data: Encourage researchers to make their code and datasets openly accessible to promote transparency and reproducibility in the evaluation process. This will enable other researchers to replicate experiments and validate results.

Community Standards: Establish community standards and best practices for evaluating distributed inference techniques, including guidelines on experimental design, result reporting, and statistical analysis.

Peer Review Criteria: Incorporate standardized evaluation criteria into the peer review process for research papers in the field of distributed inference. Reviewers can assess the adherence to evaluation standards and the rigor of the experimental methodology.

Collaborative Efforts: Encourage collaboration and knowledge sharing among researchers to collectively define evaluation standards and drive the adoption of best practices in the field.

By implementing these strategies, the evaluation of distributed inference techniques can be standardized, leading to more robust comparisons, reproducible results, and advancements in the development of efficient and scalable distributed systems for deep learning inference.