Hybrid Sparse Encoding for AI Layer Accuracy and Hardware Efficiency
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Solution Overview
Problem
Existing artificial intelligence-based models face challenges in balancing accuracy and performance due to the difficulty in designing sparse patterns for each convolution or Generalized Matrix Multiplication (GEMM) layer, leading to inefficient hardware utilization and energy consumption.
Innovation Solution
A hybrid sparse pattern is generated during training, adjusting sparsity ratios and block sizes for each layer, combined with dynamic sparse encoding to optimize hardware efficiency and maintain high accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If a sparse pattern is applied to reduce computational complexity, then energy consumption is reduced, but hardware efficiency deteriorates due to difficulty in designing optimal sparse patterns for each layer
Solution Approach 1:
The patent applies dynamic sparse encoding that adapts the sparsity pattern and encoding strategy based on the specific characteristics of each layer during inference. The system dynamically determines encoding parameters such as sparsity ratio and block size for each layer, allowing the hardware to efficiently process sparse computations without requiring pre-designed fixed patterns, thus resolving the contradiction between energy savings and hardware efficiency
Solution Approach 2:
The patent changes encoding parameters dynamically during inference based on layer characteristics. Different layers are assigned different sparsity ratios, block sizes, and encoding strategies according to their computational demands and sparsity levels. This parameter adaptation enables optimal balance between energy consumption and hardware utilization efficiency across different layers of the neural network
2Productivity
If sparsity ratio is increased to improve performance, then computational efficiency is improved, but model accuracy deteriorates
Solution Approach 1:
The patent applies different sparsity ratios and encoding strategies to different layers of the neural network based on their local characteristics. Critical layers that require high accuracy maintain higher density, while less critical layers can use higher sparsity for computational efficiency. This local adaptation allows the system to optimize the balance between accuracy and computational efficiency for each layer individually
Solution Approach 2:
The system dynamically adjusts the sparsity ratio and encoding parameters during inference based on the specific layer being processed. The dynamic encoding strategy adapts to the actual data characteristics and layer requirements, allowing the model to maintain accuracy in critical regions while achieving high computational efficiency in less critical layers through optimized sparse patterns
3Ease of manufacture
If fixed sparse patterns are used for each layer, then implementation is simplified, but hardware utilization becomes inefficient
Solution Approach 1:
The patent replaces fixed sparse patterns with dynamic sparse encoding that adapts to each layer's characteristics during inference. The system automatically determines optimal encoding parameters such as sparsity ratio, block size, and encoding strategy based on the layer's computational demands and data characteristics, eliminating the need for manual pattern design while maximizing hardware utilization efficiency
Solution Approach 2:
The system performs self-optimization by automatically analyzing layer characteristics and selecting appropriate encoding strategies without external intervention. The dynamic encoding mechanism self-adjusts parameters based on layer properties, achieving optimal hardware utilization without requiring complex manual configuration or pre-designed fixed patterns for each layer
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture to perform artificial intelligence-based sparse computation based on hybrid pattern and dynamic encoding are disclosed. An example apparatus includes memory, computer readable instructions, and processor circuitry to execute the computer readable instructions to: determine a hybrid sparse pattern of a selected layer of an artificial intelligence (AI)-based model, the hybrid sparse pattern having a sparsity ratio and a block pattern for the selected layer; in response to the sparsity ratio being above a threshold, reduce the sparsity ratio of the selected layer; and in response to the sparsity ratio being below the threshold, adjust the block pattern of the selected layer, the block pattern of the selected layer corresponding to an accuracy ratio.