Automated Compression and Deployment of Neural Networks on AURIX TC3xx Microcontrollers

المفاهيم الأساسية

OpTC, an end-to-end toolchain, automatically compresses, converts, and generates C code for deploying various types of neural networks on AURIX TC3xx microcontrollers, enabling efficient execution on resource-constrained embedded devices.

الملخص

The paper presents OpTC, a toolchain for automated compression and deployment of neural networks on AURIX TC3xx microcontrollers. The key highlights are:

1. OpTC supports various types of neural networks, including multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs).

2. It performs a sensitivity analysis to determine the maximum pruning rates for each layer, reducing the vast design space of pruning configurations.

3. OpTC uses a global weighted pruning (GWP) technique to iteratively prune the neural network, exploring the trade-offs between prediction quality, execution time, and memory requirements.

4. The toolchain generates optimized C code for the pruned neural network models, integrating target-specific optimizations such as operator fusion and tensor unionization.

5. Experiments on the MLPerf Tiny benchmark and an electric motor temperature prediction dataset demonstrate the effectiveness of OpTC. It provides speedups of up to 2.2x and enables deployment on microcontrollers with limited memory capacity, such as the AURIX TC32x with only 1 MB of ROM.

الإحصائيات

The execution time of the unpruned Autoencoder model is 3.8 ms.
The unpruned Autoencoder model requires 1.1 MB of ROM and 50 kB of scratchpad RAM.
The unpruned CNN for keyword spotting requires 130 kB of RAM, exceeding the available RAM of AURIX TC387 CPU2 and CPU3 (96 kB).
The unpruned TCN for electric motor temperature prediction requires 2.6 ms for inference and 52 kB of RAM.

اقتباسات

"OpTC, an end-to-end toolchain, automatically compresses, converts, and generates C code for deploying various types of neural networks on AURIX TC3xx microcontrollers, enabling efficient execution on resource-constrained embedded devices."
"Experiments on the MLPerf Tiny benchmark and an electric motor temperature prediction dataset demonstrate the effectiveness of OpTC. It provides speedups of up to 2.2x and enables deployment on microcontrollers with limited memory capacity, such as the AURIX TC32x with only 1 MB of ROM."

الرؤى الأساسية المستخلصة من

OpTC -- A Toolchain for Deployment of Neural Networks on AURIX TC3xx Microcontrollers

by Chri... في arxiv.org 04-25-2024

https://arxiv.org/pdf/2404.15833.pdf

استفسارات أعمق

How can OpTC be extended to support additional types of neural network layers or operators beyond the ones currently supported?

OpTC can be extended to support additional types of neural network layers or operators by incorporating new templates for code generation that correspond to the specific operations of the new layers. This would involve defining the mathematical operations of the new layers in the intermediate representation (IR) used by OpTC and creating parameterized C templates for these operations. Additionally, the sensitivity analysis algorithm in OpTC can be adapted to determine the pruning rates for the new layers based on their impact on the overall prediction quality. By expanding the set of supported layers and operators, OpTC can cater to a wider range of neural network architectures and applications.

What are the potential limitations or challenges in applying the global weighted pruning technique to more complex neural network architectures, such as those with skip connections or residual blocks?

Applying the global weighted pruning technique to more complex neural network architectures, like those with skip connections or residual blocks, may pose certain limitations and challenges. One challenge is that skip connections and residual blocks introduce additional pathways for information flow within the network, making it more intricate to determine the impact of pruning on the overall network performance. The sensitivity analysis in global weighted pruning may need to be adapted to account for the unique structure and functionality of these complex architectures. Moreover, the interdependencies between layers in skip connections and residual blocks could complicate the pruning process, as pruning one layer may have cascading effects on the subsequent layers. Ensuring that the pruning rates are appropriately adjusted to preserve the network's functionality and performance while reducing complexity is crucial in such scenarios.

How could the toolchain be further enhanced to provide recommendations or guidance on the optimal trade-offs between prediction quality, execution time, and memory requirements for a given application and target microcontroller?

To enhance the toolchain for providing recommendations on optimal trade-offs, OpTC could incorporate a performance optimization module that analyzes the trade-offs between prediction quality, execution time, and memory requirements. This module could utilize machine learning algorithms to explore the design space and identify Pareto-optimal solutions that balance these trade-offs effectively. By considering various metrics such as prediction accuracy, inference speed, and memory utilization, the toolchain can generate insights on the most suitable pruning configurations for a given application and target microcontroller. Additionally, incorporating a visualization component that displays the trade-off relationships graphically could help users understand the implications of different pruning decisions on the overall performance metrics. This enhanced functionality would empower users to make informed decisions when optimizing neural network models for deployment on resource-constrained microcontrollers.