LegoNet: Memory Footprint Reduction Through Block Weight Clustering
arXiv:2603.06606v1 Announce Type: new Abstract: As the need for neural network-based applications to become more accurate and powerful grows, so too does their size and memory footprint. With embedded devices, whose cache and RAM are limited, this growth hinders their ability to leverage state-of-the-art neural network architectures. In this work, we propose \textbf{LegoNet}, a compression technique that \textbf{constructs blocks of weights of the entire model regardless of layer type} and clusters these induced blocks. Using blocks instead of individual values to cluster the weights, we were able to compress ResNet-50 trained for Cifar-10 and ImageNet with only 32 4x4 blocks, compressing the memory footprint by over a factor of \textbf{64x without having to remove any weights} or changing the architecture and \textbf{no loss to accuracy}, nor retraining or any data, and show how to find an arrangement of 16 4x4 blocks that gives a compression ratio of \textbf{128x with less than 3\%
arXiv:2603.06606v1 Announce Type: new Abstract: As the need for neural network-based applications to become more accurate and powerful grows, so too does their size and memory footprint. With embedded devices, whose cache and RAM are limited, this growth hinders their ability to leverage state-of-the-art neural network architectures. In this work, we propose \textbf{LegoNet}, a compression technique that \textbf{constructs blocks of weights of the entire model regardless of layer type} and clusters these induced blocks. Using blocks instead of individual values to cluster the weights, we were able to compress ResNet-50 trained for Cifar-10 and ImageNet with only 32 4x4 blocks, compressing the memory footprint by over a factor of \textbf{64x without having to remove any weights} or changing the architecture and \textbf{no loss to accuracy}, nor retraining or any data, and show how to find an arrangement of 16 4x4 blocks that gives a compression ratio of \textbf{128x with less than 3\% accuracy loss}. This was all achieved with \textbf{no need for (re)training or fine-tuning}.
Executive Summary
The article proposes LegoNet, a novel compression technique for neural networks that reduces memory footprint by clustering weights into blocks. This approach achieves significant compression ratios without sacrificing accuracy or requiring retraining. For instance, ResNet-50 trained on Cifar-10 and ImageNet can be compressed by a factor of 64x with no loss in accuracy, and by 128x with less than 3% accuracy loss. This breakthrough has significant implications for deploying state-of-the-art neural networks on embedded devices with limited cache and RAM.
Key Points
- ▸ LegoNet constructs blocks of weights from the entire model, regardless of layer type
- ▸ Weight clustering using blocks achieves high compression ratios without sacrificing accuracy
- ▸ No retraining or fine-tuning is required, making it a practical solution for deployment on embedded devices
Merits
Efficient Compression
LegoNet's block-based weight clustering approach enables significant reductions in memory footprint without compromising model accuracy.
Flexibility
The technique can be applied to various neural network architectures and datasets, making it a versatile solution for a range of applications.
Demerits
Computational Overhead
The process of constructing and clustering blocks may introduce additional computational overhead, potentially impacting inference speed.
Limited Scalability
The effectiveness of LegoNet for very large models or complex datasets may be limited, requiring further research to address these scenarios.
Expert Commentary
The LegoNet technique represents a significant breakthrough in neural network compression, offering a promising solution for deploying complex models on resource-constrained devices. By clustering weights into blocks, the approach achieves remarkable compression ratios without sacrificing accuracy. However, further research is needed to address potential limitations, such as computational overhead and scalability. Nevertheless, LegoNet has the potential to democratize access to advanced neural networks, enabling a wide range of applications, from smart home devices to autonomous vehicles.
Recommendations
- ✓ Further research on optimizing the block construction and clustering process to minimize computational overhead
- ✓ Exploring the application of LegoNet to other domains, such as natural language processing and recommender systems