Ramanujan Bipartite Graph Products for Efficient Block Sparse Neural Networks

Understand

Sparse neural networks are shown to give accurate predictions competitive to denser versions, while also minimizing the number of arithmetic operations performed.

• However current hardware like GPU's can only exploit structured sparsity patterns for better efficiency.
• Hence the run time of a sparse neural network may not correspond to the arithmetic operations required.
• In this work, we propose RBGP( Ramanujan Bipartite Graph Product) framework for generating structured multi level block sparse neural networks by using the theory of Graph products.