Parallel Complexity of Forward and Backward Propagation

TL;DR

This paper explores the parallelization of forward and backward propagation in neural networks by formulating them as solutions to triangular systems, enabling efficient parallel algorithms and model parallelism.

Contribution

It introduces a unified framework for parallelizing propagation in neural networks using triangular system solutions, applicable to various network architectures.

Findings

Backward propagation in FNNs can be parallelized in O(log k) steps.

Backward propagation in RNNs can be parallelized in O(log k log τ) steps.

The approach generalizes to arbitrary layers using Jacobians.

Abstract

We show that the forward and backward propagation can be formulated as a solution of lower and upper triangular systems of equations. For standard feedforward (FNNs) and recurrent neural networks (RNNs) the triangular systems are always block bi-diagonal, while for a general computation graph (directed acyclic graph) they can have a more complex triangular sparsity pattern. We discuss direct and iterative parallel algorithms that can be used for their solution and interpreted as different ways of performing model parallelism. Also, we show that for FNNs and RNNs with $k$ layers and $\tau$ time steps the backward propagation can be performed in parallel in O($\log k$) and O($\log k \log \tau$) steps, respectively. Finally, we outline the generalization of this technique using Jacobians that potentially allows us to handle arbitrary layers.