Structured Sparsity Training for Lower-Memory Neural Networks
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Solution Overview
Problem
Existing neural network architectures face challenges in optimizing power performance and memory requirements due to the inherent randomness of sparsity, leading to inefficient coupling between the control and data planes.
Innovation Solution
Implementing structured sparsity mechanisms in neural network architectures that leverage a priori knowledge of weight and activation patterns, allowing for static execution of operations without continuous control plane intervention, and guiding training to maximize sparsity during the compilation phase.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional neural network architectures are used, then the network can perform basic computations, but memory requirements and power consumption are high due to lack of sparsity exploitation
Solution Approach 1:
The patent applies local quality by introducing structured sparsity patterns (e.g., row-sparsity, column-sparsity, block-sparsity) that vary across different regions of the weight matrix. Instead of uniform treatment, the system identifies and exploits local zero patterns specific to each region, allowing memory optimization tailored to the actual data distribution and computational needs of each layer or module.
Solution Approach 2:
The system dynamically adjusts sparsity parameters during training and inference, including the degree and type of sparsity applied to different weight matrices. The compiler determines optimal sparsity patterns based on hardware capabilities and data characteristics, while the runtime system adapts these patterns to maximize computational efficiency and minimize memory access operations.
2Quantity of substance
If structured sparsity mechanisms are implemented, then memory requirements are reduced, but the complexity of the training process increases due to need for sparsity-guided optimization
Solution Approach 1:
The system performs preliminary analysis during the compilation phase to identify structured sparsity patterns in the weight matrices. The compiler examines the data distribution and computational graph to pre-determine optimal sparsity patterns, which are then encoded into the execution plan. This preliminary action eliminates the need for complex runtime analysis and simplifies the training process by providing guidance rather than requiring full optimization from scratch.
Solution Approach 2:
The patent introduces a sparsity-guided training framework that acts as an intermediary between the raw training data and the final model. This framework includes specialized modules that analyze data characteristics, determine sparsity patterns, and generate optimized training objectives. The intermediary layer translates complex optimization problems into more manageable tasks, reducing the overall complexity of training while achieving structured sparsity.
3Quantity of substance
If random sparsity is used, then the network can reduce parameter count, but the coupling between control and data planes becomes inefficient
Solution Approach 1:
The system introduces asymmetric sparsity patterns where different rows, columns, or blocks of the weight matrix have different sparsity characteristics. This asymmetry allows the system to optimize for specific computational patterns and data access requirements, creating more efficient control-data plane coupling by aligning sparsity structures with the actual computational graph and memory access patterns.
Solution Approach 2:
The patent implements dynamic sparsity adaptation where the sparsity pattern is not fixed but can change based on runtime conditions. The system dynamically adjusts which weights are activated and how they are accessed, allowing flexible optimization of the control-data plane coupling for different input sizes, data distributions, and hardware configurations without requiring manual reconfiguration.
Data Source
AI summary
A novel and useful system and method of improved power performance and lowered memory requirements for an artificial neural network based on packing memory utilizing several structured sparsity mechanisms. The invention applies to neural network (NN) processing engines adapted to implement mechanisms to search for structured sparsity in weights and activations, resulting in a considerably reduced memory usage. The sparsity guided training mechanism synthesizes and generates structured sparsity weights. A compiler mechanism within a software development kit (SDK), manipulates structured weight domain sparsity to generate a sparse set of static weights for the NN. The structured sparsity static weights are loaded into the NN after compilation and utilized by both the structured weight domain sparsity mechanism and the structured activation domain sparsity mechanism. The application of structured sparsity lowers the span of search options and creates a relatively loose coupling between the data and control planes.