Sparse Weight Compression With Meta-Data for Bandwidth-Limited Inference
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Solution Overview
Problem
The increasing complexity of neural network sizes and memory bandwidth limitations in deep learning models cause performance bottlenecks, particularly due to large weight matrices and sparsity, leading to high Total Cost of Ownership (TCO) and resource challenges in existing solutions like re-training with structured sparsity.
Innovation Solution
Implement unstructured sparsity to compress neural network weights, utilizing advanced vector and matrix extensions (AVX, AMX) for efficient compression and decompression, leveraging meta-data to manage cache usage and reduce memory bandwidth requirements while maintaining accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural network sizes are increased to handle diverse use cases, then model complexity and accuracy are improved, but memory bandwidth requirements and TCO increase
Solution Approach 1:
The patent extracts and removes redundant information from neural network weights by applying unstructured sparsity techniques. Specifically, it identifies and eliminates weights below certain thresholds, keeping only the most significant weights that contribute to model accuracy, thereby reducing memory bandwidth requirements while maintaining performance
Solution Approach 2:
The patent changes the density parameter of weight matrices by transforming dense representations into sparse representations with controlled sparsity levels (e.g., 80% sparsification). This parameter change reduces the quantity of data that needs to be transferred across memory bandwidth while preserving the essential information needed for accurate inference
2Quantity of substance
If re-training with structured sparsity is applied to reduce memory usage, then resource consumption is reduced, but model accuracy and performance deteriorate
Solution Approach 1:
The patent employs dynamic sparsity patterns that are not constrained by fixed structured formats. Unlike structured sparsity that requires regular patterns (e.g., block sparsity), this approach allows each weight to be independently pruned based on its magnitude, enabling more flexible and accurate resource optimization without sacrificing model performance
Solution Approach 2:
The patent applies magnitude-based thresholding to transform dense weight matrices into sparse matrices with controlled sparsity levels. By setting dynamic thresholds during the sparsification process, it achieves over 80% sparsification while maintaining model accuracy, directly addressing the contradiction between resource consumption and model performance
3Quantity of substance
If unstructured sparsity is applied to compress weights, then memory bandwidth requirements are reduced, but computational complexity for compression and decompression increases
Solution Approach 1:
The patent performs weight sparsification as a preliminary action during model deployment or initialization. By pre-computing the sparse weight matrices and storing them in compressed format, it eliminates the need for complex compression operations during inference, reducing real-time computational complexity while maintaining reduced memory bandwidth requirements
Solution Approach 2:
The sparsification process leverages the inherent magnitude information already present in the trained weight matrices. By using simple magnitude-based thresholding rather than complex optimization algorithms, the system achieves compression with minimal additional computational overhead, allowing the model to essentially compress itself using its existing structure
4Quantity of substance
If weight compression is applied to reduce TCO, then resource costs are reduced, but inference performance may deteriorate
Solution Approach 1:
The patent carefully controls the sparsity parameter to achieve over 80% sparsification while maintaining inference performance. By adjusting the sparsity level parameter and using magnitude-based selection, it ensures that only the most important weights are retained, thereby reducing resource costs without significantly deteriorating inference performance
Solution Approach 2:
The patent replaces complex memory access patterns with simpler sparse matrix operations. By substituting dense matrix multiplications with sparse matrix operations that skip zero elements, it reduces memory bandwidth requirements and resource costs while maintaining or even improving inference performance through more efficient computation
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture to perform weight and activation compression and decompression are disclosed. An example apparatus includes memory, instructions in the apparatus, and processor circuitry to execute the instructions to execute a compression operation to obtain compressed data corresponding to weights in a weight matrix, and determine meta-data associated with the weight matrix, a first portion of the meta-data indicative of whether the weight matrix is compressed, a second portion of the meta-data indicative of a cache size of the compressed data, and a third portion of the meta-data indicative of the compression operation executed to obtain the compressed data.