Self-Compressing Neural Networks
The paper "Self-Compressing Neural Networks" presents a method to reduce neural network size, maintaining accuracy while using only 3% of bits and 18% of weights, accepted for the 2023 DL-Hardware conference.
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The paper titled "Self-Compressing Neural Networks" by Szabolcs Cséfalvay and James Imber addresses the challenge of reducing the size of neural networks, which significantly impacts execution time, power consumption, bandwidth, and memory usage. The authors introduce a method called Self-Compression, which aims to achieve two main objectives: eliminating redundant weights and minimizing the number of bits needed to represent the remaining weights. This is accomplished through a generalized loss function designed to reduce the overall size of the network. Experimental results indicate that it is possible to maintain floating-point accuracy while retaining only 3% of the bits and 18% of the weights in the network. The work has been accepted for presentation at the 2023 DL-Hardware Co-Design for AI Acceleration conference, highlighting its relevance in the field of machine learning and artificial intelligence. The proposed method offers a straightforward approach to optimizing neural networks without requiring specialized hardware, making it accessible for efficient training and inference.
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- Several commenters highlight the importance of prior work in neural network sparsity and optimization that the paper may not have referenced.
- There is a shared interest in the potential for self-organizing models that adapt their size and structure based on the task at hand.
- Some express excitement about the implications of this research for mimicking biological processes, such as neuroplasticity.
- Questions arise regarding the advantages of this method compared to traditional post-training compression techniques.
- Commenters speculate on the future of neural networks and their potential impact on AGI development.
14 commentsI'd want a model that scales up and down depending on the task given at inference, and a model that doesn't have a fixed size when starting the training. Shouldn't it specialize over training progress, when seeing more tokens, and grow larger where needed? Without some human fixing a size beforehand?
Self-organization is a fascinating topic to me. This last year I've been working on Self-Organizing Gaussian Splats [0]. With a lot of squinting, this lives in a similar space as the Self-Compressing Neural Networks from the link above. The idea of the Gaussians was to build on Self-Organizing Maps (lovely 90s concept, look for some GIFs if you don't know it), and use that to represent 3D scenes in a memory-efficient way. By mapping attributes into a locally smooth 2D grid. It's quite a simple algorithm, but works really well, and better than many quite complicated coding schemes. So this has me excited that we'll (re-)discover great methods in this space in the near future.
[0]: https://fraunhoferhhi.github.io/Self-Organizing-Gaussians/
This paper is a long way from implementing synaptic pruning/strengthening/weakening, neurogenesis, or synaptogenesis but it’s the first one I’ve seen where the network is self optimizing.
https://arxiv.org/abs/1712.01312
Abstract: "We propose a practical method for L0 norm regularization for neural networks: pruning the network during training by encouraging weights to become exactly zero. Such regularization is interesting since (1) it can greatly speed up training and inference, and (2) it can improve generalization. [...]"
My take on it: I find it difficult to generalize the notion of layer removal when the bit depth of that layer goes to zero. It's wouldn't be straight forward although the authors provide equation 5. It feels like lot of information is missing in this work to even reproduce it. And authors do only 1 case study.
I believe some implementation is required to understand the authors completely. Example, optimizer modification for layer when it is removed in training.
If i can sort the lines of the matrix, which is probably defined by how the token embedding is setup, i could potentially zero out weights which do not contribute at all and have areas of zeros i could mark and skip?
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