| Paper ID | MLSP-18.4 |
| Paper Title |
OPTIMAL SELECTION OF MATRIX SHAPE AND DECOMPOSITION SCHEME FOR NEURAL NETWORK COMPRESSION |
| Authors |
Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán, University of California, Merced, United States |
| Session | MLSP-18: Matrix Factorization and Applications |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 14:00 - 14:45 |
| Presentation Time: | Wednesday, 09 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [MLR-MFC] Matrix factorizations/completion |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
When applying the low-rank decomposition to neural networks, tensor-shaped weights need to be reshaped into a matrix first. While many matrix reshapes are possible, some of them induce a low-rank decomposition scheme that can be more efficiently implemented as a sequence of layers. This poses the following problem: how should one select both the matrix reshape and associated low-rank decomposition scheme in order to compress a neural network so that its implementation is as efficient as possible? We formulate this problem as a mixed-integer optimization over the weights, ranks, and decompositions schemes; and we provide an efficient alternating optimization algorithm involving two simple steps: a step over the weights of the neural network (solved by SGD), and a step over the ranks and decomposition schemes (solved by an SVD). Our algorithm automatically selects the most suitable ranks and decomposition schemes to efficiently reduce compression costs (e.g., FLOPs) of various networks. |
