Gradient-Based Weight Pruning for AI Model Optimization
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Solution Overview
Problem
Current memory systems struggle to provide high throughput and low latency for artificial intelligence (AI) applications, leading to increased demands for data movement bandwidth and storage capacity that exceed the capabilities of existing data centers and devices.
Innovation Solution
A device and method for determining priority values for elements of a weight matrix in an AI model based on a gradient of a loss function, generating a pruned weights matrix by applying a mask to the weight matrix, and processing queries using an updated AI model with the pruned weights matrix.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If weight pruning is applied to reduce model size and computational requirements, then energy consumption is reduced and resource utilization is improved, but model performance may degrade
Solution Approach 1:
The patent implements feedback mechanisms by computing priority values based on the gradient of the loss function with respect to weights. This gradient-based feedback allows the system to identify which weights are most critical for maintaining model performance, enabling selective pruning that preserves reliability while reducing energy consumption.
Solution Approach 2:
The patent changes the parameter representation by transforming weights into priority values through gradient computation. This parameter transformation enables the system to make informed decisions about which weights to prune, balancing model size reduction with performance maintenance through threshold-based selection.
2Quantity of substance
If more weights are pruned to reduce storage requirements, then storage capacity demands are reduced, but data access efficiency may be impacted
Solution Approach 1:
The patent applies local quality by treating different weights differently based on their individual priority values. Instead of uniform pruning, the system selectively prunes only those weights with low priority values (below the threshold), maintaining high-priority weights that are critical for fast and accurate data access, thus balancing storage reduction with access efficiency.
3Measurement precision
If priority values are determined using gradient of loss function, then pruning accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies partial action by computing priority values only for weights that need to be evaluated for pruning, rather than performing exhaustive analysis on all model parameters. The threshold-based selection further limits computation to only those weights near the decision boundary, reducing overall computational complexity while maintaining pruning accuracy.
Data Source
AI summary
Provided are systems, methods, and apparatuses of data processing for machine learning. In one or more examples, the systems, devices, and methods include determining priority values for elements of a weight matrix based on a gradient of a loss function of an AI model and the weight matrix; determining an index value based on a number of elements in the weight importance matrix and a sparsity ratio; determining a threshold based on sorting the elements of the weight importance matrix in sequential order and determining a value of an element of the sorted weight importance matrix based on using the index value as an index of the sorted weight importance matrix; determining a pruned weights matrix based on the threshold; and processing a query using an updated AI model, the updated AI model being based on the pruned weights matrix being implemented in the AI model.