A Directed-Evolution Method for Sparsification and Compression of Neural Networks with Application to Object Identification and Segmentation and considerations of optimal quantization using small number of bits
This work introduces Directed-Evolution (DE) method for sparsification of neural networks, where the relevance of parameters to the network accuracy is directly assessed and the parameters that produce the least effect on accuracy when tentatively zeroed are indeed zeroed. DE method avoids a potenti...
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Zusammenfassung: | This work introduces Directed-Evolution (DE) method for sparsification of
neural networks, where the relevance of parameters to the network accuracy is
directly assessed and the parameters that produce the least effect on accuracy
when tentatively zeroed are indeed zeroed. DE method avoids a potentially
combinatorial explosion of all possible candidate sets of parameters to be
zeroed in large networks by mimicking evolution in the natural world. DE uses a
distillation context [5]. In this context, the original network is the teacher
and DE evolves the student neural network to the sparsification goal while
maintaining minimal divergence between teacher and student. After the desired
sparsification level is reached in each layer of the network by DE, a variety
of quantization alternatives are used on the surviving parameters to find the
lowest number of bits for their representation with acceptable loss of
accuracy. A procedure to find optimal distribution of quantization levels in
each sparsified layer is presented. Suitable final lossless encoding of the
surviving quantized parameters is used for the final parameter representation.
DE was used in sample of representative neural networks using MNIST,
FashionMNIST and COCO data sets with progressive larger networks. An 80 classes
YOLOv3 with more than 60 million parameters network trained on COCO dataset
reached 90% sparsification and correctly identifies and segments all objects
identified by the original network with more than 80% confidence using 4bit
parameter quantization. Compression between 40x and 80x. It has not escaped the
authors that techniques from different methods can be nested. Once the best
parameter set for sparsification is identified in a cycle of DE, a decision on
zeroing only a sub-set of those parameters can be made using a combination of
criteria like parameter magnitude and Hessian approximations. |
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DOI: | 10.48550/arxiv.2206.05859 |