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This work introduces Directed-Evolution (DE) method for sparsification ofneural networks, where the relevance of parameters to the network accuracy isdirectly assessed and the parameters that produce the least effect on accuracywhen tentatively zeroed are indeed zeroed. DE method avoids a potentiallycombinatorial explosion of all possible candidate sets of parameters to bezeroed in large networks by mimicking evolution in the natural world. DE uses adistillation context [5]. In this context, the original network is the teacherand DE evolves the student neural network to the sparsification goal whilemaintaining minimal divergence between teacher and student. After the desiredsparsification level is reached in each layer of the network by DE, a varietyof quantization alternatives are used on the surviving parameters to find thelowest number of bits for their representation with acceptable loss ofaccuracy. A procedure to find optimal distribution of quantization levels ineach sparsified layer is presented. Suitable final lossless encoding of thesurviving 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 classesYOLOv3 with more than 60 million parameters network trained on COCO datasetreached 90% sparsification and correctly identifies and segments all objectsidentified by the original network with more than 80% confidence using 4bitparameter quantization. Compression between 40x and 80x. It has not escaped theauthors that techniques from different methods can be nested. Once the bestparameter set for sparsification is identified in a cycle of DE, a decision onzeroing only a sub-set of those parameters can be made using a combination ofcriteria like parameter magnitude and Hessian approximations.

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