AI Integrated Circuit Architecture for Sparse Neural Network Processing
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Solution Overview
Problem
Existing technologies face challenges in efficiently processing and executing Artificial Intelligence operations, particularly for neural networks with sparse weights, due to high memory and power consumption requirements, which limits compute utilization and increases memory needs.
Innovation Solution
An integrated circuit with a tailored architecture, including a scheduler, arithmetic logic units, and random access memories, is designed to efficiently process neural networks with sparse weights by using a bit mask to skip unnecessary calculations and optimize data loading, reducing power consumption and memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional processing architectures are used for neural networks with sparse weights, then all weight values are processed uniformly, but this results in high memory and power consumption without utilizing compute elements efficiently
Solution Approach 1:
The patent extracts and processes only the non-zero weight values from the neural network weights, separating them from the sparse structure. By identifying and processing only the relevant compute elements (non-zero weights), the system avoids unnecessary computations and memory accesses, thereby improving compute utilization while reducing power consumption.
Solution Approach 2:
The patent dynamically configures the processing architecture to adapt to the sparse structure of neural network weights. The system dynamically identifies which compute elements are active (non-zero weights) and adjusts the processing pipeline accordingly, enabling efficient utilization of compute elements while minimizing energy consumption on inactive elements.
2Adaptability or versatility
If conventional processing architectures process all weight values uniformly, then implementation is simple, but memory requirements increase significantly
Solution Approach 1:
The patent segments the weight data into non-zero and zero elements, processing only the non-zero segment. This segmentation allows the system to handle various AI models with different sparsity patterns while significantly reducing memory requirements by storing and processing only the relevant weight values along with their corresponding indices.
Solution Approach 2:
The patent changes the representation parameters of weight data from dense format to sparse format (storing only non-zero values and their indices). This parameter transformation enables the system to support various AI models with different sparsity characteristics while reducing memory requirements proportionally to the sparsity level.
3Reliability
If all compute elements are activated for processing, then computation is thorough, but power consumption increases
Solution Approach 1:
The patent extracts only the non-zero weight values and their corresponding compute elements, excluding zero weights from processing. This extraction ensures that computation is performed only on relevant elements, maintaining computation accuracy for actual neural network operations while eliminating energy waste on zero weight computations.
Solution Approach 2:
The patent applies partial action by processing only the necessary subset of compute elements (those corresponding to non-zero weights) rather than all compute elements. This partial processing maintains computation accuracy for the actual neural network function while avoiding excessive energy consumption on unnecessary computations.
Data Source
AI summary
A method, apparatus, and system are discussed to efficiently process and execute Artificial Intelligence operations. An integrated circuit has a tailored architecture to process and execute Artificial Intelligence operations, including computations for a neural network having weights with a sparse value. The integrated circuit contains at least a scheduler, one or more arithmetic logic units, and one or more random access memories configured to cooperate with each other to process and execute these computations for the neural network having weights with the sparse value.