Tensor Rematerialization for Neural Networks Using Precedence Constraints
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Solution Overview
Problem
The deployment of large neural networks on edge devices is hindered by limited memory resources, as conventional approaches to tensor rematerialization, such as mixed integer linear programs, become inefficient when scaling to large graphs due to high computational complexity and suboptimal solutions.
Innovation Solution
A processor-implemented method determines retention intervals and identifies nodes for recomputation in artificial neural networks based on precedence constraints, optimizing memory utilization and compute time by using constrained programming to enforce data dependencies and memory constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional mixed integer linear programs are used for tensor rematerialization, then memory footprint is reduced, but computational complexity increases and solution optimality decreases when scaling to large graphs
Solution Approach 1:
The patent segments the rematerialization optimization problem into two distinct phases: (1) determining retention intervals for each node based on precedence constraints, and (2) selecting which nodes to recompute based on those intervals. This segmentation transforms the original complex mixed integer linear program into simpler, more manageable subproblems that can be solved efficiently without requiring heavy computational resources.
Solution Approach 2:
The patent performs preliminary determination of retention intervals before selecting nodes for recomputation. By pre-calculating when each node's output should be retained in memory based on data dependency constraints, the system eliminates the need for complex real-time optimization during the node selection phase, thereby reducing overall computational complexity while maintaining solution quality.
2Productivity
If more nodes are retained in memory, then computation time is reduced, but memory footprint increases
Solution Approach 1:
The patent changes the parameter representation from binary node selection variables to retention interval variables. By representing the problem in terms of time intervals during which node outputs are retained, the system can directly control memory usage patterns and optimize the balance between computation time and memory footprint through interval manipulation rather than complex node selection.
Solution Approach 2:
The patent introduces dynamic retention intervals that adapt to the specific data dependency structure of each neural network graph. Rather than using static memory retention strategies, the system dynamically determines how long each node's output should be kept in memory based on when it is needed by subsequent operations, enabling flexible optimization of the computation-time-memory tradeoff.
3Quantity of substance
If rematerialization is applied to reduce memory usage, then memory footprint is reduced, but computational overhead increases
Solution Approach 1:
The patent incorporates feedback through precedence constraints that capture the data dependency relationships between nodes. By using these constraints to guide retention interval determination, the system ensures that rematerialization decisions are made based on actual computational needs, avoiding unnecessary recomputations and thereby reducing computational overhead while maintaining memory efficiency.
Data Source
AI summary
A processor-implemented method for rematerialization for an artificial neural network (ANN) includes receiving a graph representing the ANN. The graph includes multiple nodes connected by edges and each node represents an operation. Retention intervals for the nodes are determined based on a precedence constraint for the nodes. The retention intervals correspond to a time interval for retaining each node output in a local memory. One of the nodes to recompute is determined based on the retention intervals.