Binary normalized neural networks achieve 32x memory reduction with single-bit parameters while maintaining performance
By
PaulHoule
Warm and crisp on the edges. A bagel with a bit of bite.
Summary
This paper introduces binary normalized neural network layers where all parameters (kernel weights and biases) are constrained to single-bit values (0 or 1), reducing memory usage by 32x compared to conventional 32-bit models. The approach works across layer types including fully connected, convolutional, and attention layers. Tests on image classification and language modeling (next-token prediction) show that binary normalized models achieve nearly equivalent performance to their 32-bit counterparts. The method can be implemented on existing hardware using 1-bit arrays without requiring specialized electronics, enabling deployment on simple, cheap hardware like mobile devices or CPUs.
Key quotes
· 5 pulledIn this work, a novel type of neural network layers and models is developed that uses only single-bit parameters.
The results show that models with binary normalized layers present almost the same results obtained by equivalent models with real 32-bit parameters.
The binary normalized layers allow to develop models that use 32 times less memory than current models and have equivalent performance.
The binary normalized layers can be easily implemented on current computers using 1-bit arrays, and do not require the development of dedicated electronic hardware.
This novel type of layers opens a new era for large neural network models with reduced memory requirements that can be deployed using simple and cheap hardware, such as mobile devices or only cpus.
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