洞察 - Embedded systems - # System-Level Design Space Exploration for High-Level Synthesis under End-to-End Latency Constraints

Optimizing End-to-End Latency for Multi-Component Embedded Systems using High-Level Synthesis Design Space Exploration

Q: How can the EtoE-DSE approach be extended to handle more complex communication protocols and synchronization mechanisms beyond the event handshake model?

The EtoE-DSE approach can be extended to accommodate more complex communication protocols and synchronization mechanisms by integrating additional models that capture the intricacies of these protocols. For instance, protocols such as Message Queuing, Publish-Subscribe, or even more sophisticated mechanisms like Time-Triggered Protocols (TTP) can be incorporated into the existing framework. This can be achieved by developing a more generalized communication model that allows for the definition of various message types, priorities, and delivery guarantees. To implement this, the EtoE-DSE framework could utilize a modular architecture where different communication protocols can be plugged in as needed. Each protocol would have its own latency estimation model that accounts for the specific characteristics of the communication method, such as message size, transmission delays, and acknowledgment requirements. Additionally, synchronization mechanisms like Mutexes, Semaphores, or even more advanced techniques like Barrier Synchronization could be modeled to ensure that the timing constraints of the system are respected. Furthermore, the pathfinding algorithm within the EtoE-DSE could be enhanced to consider these new communication and synchronization models, allowing for a more comprehensive analysis of the end-to-end latency. By doing so, the approach would not only maintain its focus on latency constraints but also provide a more holistic view of the system's performance, accommodating the complexities introduced by various communication protocols.

Q: What are the potential challenges and limitations of the EtoE-DSE approach when applied to systems with highly dynamic and unpredictable timing behavior?

When applied to systems with highly dynamic and unpredictable timing behavior, the EtoE-DSE approach faces several challenges and limitations. One significant challenge is the accurate estimation of end-to-end latency. In systems where timing can vary due to factors such as workload fluctuations, environmental changes, or resource contention, the static models used for latency estimation may become inadequate. This can lead to suboptimal design choices that do not meet the real-time requirements of the system. Another limitation is the scalability of the design space exploration process. As the complexity of the system increases, particularly in dynamic environments, the number of potential configurations can grow exponentially. This makes exhaustive exploration infeasible and can hinder the effectiveness of heuristic or genetic algorithms used in the DSE process. The dynamic nature of the system may also require real-time adjustments to the design space exploration strategy, which could complicate the implementation of the EtoE-DSE methodology. Additionally, the interaction between components in a dynamic system can introduce unforeseen dependencies and timing constraints that are not captured in the initial design models. This necessitates a more adaptive approach to DSE, where the methodology can respond to changes in system behavior in real-time, potentially requiring the integration of machine learning techniques to predict and adapt to these changes.

Q: How can the EtoE-DSE methodology be adapted to explore the design space of heterogeneous computing platforms, such as those combining CPUs, GPUs, and specialized accelerators?

To adapt the EtoE-DSE methodology for exploring the design space of heterogeneous computing platforms, several strategies can be employed. First, the framework should be extended to support multiple types of processing units, including CPUs, GPUs, and specialized accelerators like FPGAs or ASICs. This can be achieved by developing a unified abstraction layer that allows the EtoE-DSE to interact with different hardware architectures seamlessly. The latency estimation model must also be modified to account for the unique characteristics of each processing unit. For instance, GPUs may have different latency profiles due to their parallel processing capabilities, while FPGAs may offer lower latency for specific tasks but require additional configuration time. The EtoE-DSE approach should incorporate these factors into its latency estimation algorithms to provide accurate predictions for heterogeneous systems. Moreover, the design space segmentation process can be enhanced to consider the resource availability and performance characteristics of each processing unit. This involves creating a mapping strategy that optimally assigns tasks to the most suitable processing unit based on their computational requirements and the current system state. The frequency-based design space segmentation (FDSS) process can be adapted to include considerations for the different operating frequencies and power profiles of each type of processor. Finally, the optimization algorithms used in the LCSO process should be tailored to handle the multi-objective nature of heterogeneous systems, where trade-offs between energy consumption, performance, and area may differ significantly across different processing units. By incorporating these adaptations, the EtoE-DSE methodology can effectively explore the design space of heterogeneous computing platforms, ensuring that the resulting configurations meet the stringent requirements of modern embedded systems.

核心概念

This paper presents a novel system-level High-Level Synthesis (HLS) Design Space Exploration (DSE) approach, called EtoE-DSE, that accommodates end-to-end (EtoE) latency and variable timing constraints for complex multi-component application-specific embedded systems.

摘要

The paper presents the EtoE-DSE approach, which comprises three key steps:

EtoE Latency Estimation Model:
- Constructs a system-level state-based graph to represent the interactions and dependencies between the components.
- Employs an EtoE pathfinding (EPF) algorithm to identify all required paths and sub-paths between any two endpoints within the targeted systems, considering the EtoE latency constraints.
- Estimates the worst-case latency for each path, taking into account the variable timing constraints.
Frequency-based Design Space Segmentation (FDSS):
- Divides the original design space into manageable subspaces by segmenting the frequency combinations.
- Prunes less optimal solutions in terms of energy and area while ensuring the variable timing constraints are met.
Latency-Constrained Segmentation Optimization (LCSO):
- Leverages an elite genetic algorithm to efficiently and accurately explore the system-level Pareto-optimal design solutions under the EtoE latency constraints.
- Incorporates dynamic Pareto-optimal elitism, a custom genetic representation, and parallelization to improve the search process.

The authors evaluate the EtoE-DSE approach using a real-world use case of an autonomous driving subsystem (ADS). The results show that EtoE-DSE substantially outperforms prior DSE approaches, improving the quality of results by up to 89.26%, while efficiently identifying Pareto-optimal configurations in terms of energy and area under EtoE latency constraints.

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The original design space for the ADS system is calculated to be 5.291e+128.
The minimum and maximum scaled frequencies for MCC alternatives are set at 2 and 108 MHz, respectively.
The frequency interval is selected as 5 MHz, and the frequency pin constraint is 4.

引用

"EtoE-DSE employs a latency estimation model and a pathfinding algorithm to identify and estimate the EtoE latency for paths between any endpoints."
"The frequency-based segmentation process segments and prunes the design space, alongside a latency-constrained optimization algorithm for efficiently and accurately exploring the system-level design space."
"On average, compared to the original design space, the FDSS process reduced the design space by 2.15e+55× without reducing the quality of results (QoR)."

从中提取的关键见解

System-Level Design Space Exploration for High-Level Synthesis under End-to-End Latency Constraints

by Yuchao Liao,... 在 arxiv.org 09-26-2024

https://arxiv.org/pdf/2408.10431.pdf

System-Level Design Space Exploration for High-Level Synthesis under End-to-End Latency Constraints

更深入的查询

How can the EtoE-DSE approach be extended to handle more complex communication protocols and synchronization mechanisms beyond the event handshake model?

The EtoE-DSE approach can be extended to accommodate more complex communication protocols and synchronization mechanisms by integrating additional models that capture the intricacies of these protocols. For instance, protocols such as Message Queuing, Publish-Subscribe, or even more sophisticated mechanisms like Time-Triggered Protocols (TTP) can be incorporated into the existing framework. This can be achieved by developing a more generalized communication model that allows for the definition of various message types, priorities, and delivery guarantees.
To implement this, the EtoE-DSE framework could utilize a modular architecture where different communication protocols can be plugged in as needed. Each protocol would have its own latency estimation model that accounts for the specific characteristics of the communication method, such as message size, transmission delays, and acknowledgment requirements. Additionally, synchronization mechanisms like Mutexes, Semaphores, or even more advanced techniques like Barrier Synchronization could be modeled to ensure that the timing constraints of the system are respected.
Furthermore, the pathfinding algorithm within the EtoE-DSE could be enhanced to consider these new communication and synchronization models, allowing for a more comprehensive analysis of the end-to-end latency. By doing so, the approach would not only maintain its focus on latency constraints but also provide a more holistic view of the system's performance, accommodating the complexities introduced by various communication protocols.

What are the potential challenges and limitations of the EtoE-DSE approach when applied to systems with highly dynamic and unpredictable timing behavior?

When applied to systems with highly dynamic and unpredictable timing behavior, the EtoE-DSE approach faces several challenges and limitations. One significant challenge is the accurate estimation of end-to-end latency. In systems where timing can vary due to factors such as workload fluctuations, environmental changes, or resource contention, the static models used for latency estimation may become inadequate. This can lead to suboptimal design choices that do not meet the real-time requirements of the system.
Another limitation is the scalability of the design space exploration process. As the complexity of the system increases, particularly in dynamic environments, the number of potential configurations can grow exponentially. This makes exhaustive exploration infeasible and can hinder the effectiveness of heuristic or genetic algorithms used in the DSE process. The dynamic nature of the system may also require real-time adjustments to the design space exploration strategy, which could complicate the implementation of the EtoE-DSE methodology.
Additionally, the interaction between components in a dynamic system can introduce unforeseen dependencies and timing constraints that are not captured in the initial design models. This necessitates a more adaptive approach to DSE, where the methodology can respond to changes in system behavior in real-time, potentially requiring the integration of machine learning techniques to predict and adapt to these changes.

How can the EtoE-DSE methodology be adapted to explore the design space of heterogeneous computing platforms, such as those combining CPUs, GPUs, and specialized accelerators?

To adapt the EtoE-DSE methodology for exploring the design space of heterogeneous computing platforms, several strategies can be employed. First, the framework should be extended to support multiple types of processing units, including CPUs, GPUs, and specialized accelerators like FPGAs or ASICs. This can be achieved by developing a unified abstraction layer that allows the EtoE-DSE to interact with different hardware architectures seamlessly.
The latency estimation model must also be modified to account for the unique characteristics of each processing unit. For instance, GPUs may have different latency profiles due to their parallel processing capabilities, while FPGAs may offer lower latency for specific tasks but require additional configuration time. The EtoE-DSE approach should incorporate these factors into its latency estimation algorithms to provide accurate predictions for heterogeneous systems.
Moreover, the design space segmentation process can be enhanced to consider the resource availability and performance characteristics of each processing unit. This involves creating a mapping strategy that optimally assigns tasks to the most suitable processing unit based on their computational requirements and the current system state. The frequency-based design space segmentation (FDSS) process can be adapted to include considerations for the different operating frequencies and power profiles of each type of processor.
Finally, the optimization algorithms used in the LCSO process should be tailored to handle the multi-objective nature of heterogeneous systems, where trade-offs between energy consumption, performance, and area may differ significantly across different processing units. By incorporating these adaptations, the EtoE-DSE methodology can effectively explore the design space of heterogeneous computing platforms, ensuring that the resulting configurations meet the stringent requirements of modern embedded systems.