Ristretto: A Framework for Empirical Study of Resource-Efficient Inference in Convolutional Neural Networks

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Convolutional Neural Networks (CNNs) achieve high performance in pattern recognition but demand significant computational resources.
• Deploying advanced CNNs on embedded systems is challenging due to limitations in computation, energy, and memory.
• Approximating CNNs with efficient models is crucial for practical applications in resource-constrained environments.

Purpose of the Study:

• To present Ristretto, a novel framework for approximating Convolutional Neural Networks (CNNs).
• To enable the empirical investigation of the trade-offs between number representation, word width, and classification accuracy in CNNs.
• To facilitate the deployment of deep CNNs on resource-constrained embedded systems.

Main Methods:

• Ristretto analyzes the numerical range requirements for weights, activations, and intermediate results in CNN layers.
• The framework simulates the impact of reduced word width and lower-precision arithmetic on model accuracy.
• Ristretto incorporates fine-tuning of quantized networks to optimize accuracy under specific configurations.

Main Results:

• Demonstrated that three ImageNet networks can be condensed using 8-bit dynamic fixed-point representation for weights and activations.
• Achieved this condensation with a maximum classification accuracy degradation tolerance of 1%.
• Ristretto is available as an open-source software project with significant community interest.

Conclusions:

• Ristretto provides an effective approach for approximating CNNs, balancing accuracy and efficiency.
• The framework enables the practical deployment of deep learning models on embedded systems.
• The open-source availability of Ristretto promotes further research and development in efficient deep learning.