LLM Matrix Multiplication Dimension Matching for Hardware Accelerators
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Solution Overview
Problem
The high overhead on processors due to the large number of matrix multiplication operations required by large language models (LLMs) hinders their efficiency in performing other operations.
Innovation Solution
A processing system is configured to optimize LLMs by modifying their matrix dimensions to match the capabilities of hardware accelerators, reducing the need for CPU involvement and increasing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the processor performs matrix multiplication operations for LLMs, then the LLM operations are executed, but the processor overhead increases drastically
Solution Approach 1:
The patent segments the processing workload by separating LLM matrix multiplication operations from general CPU tasks. Dedicated hardware accelerators handle the matrix multiplication operations while the CPU manages other operations, resolving the contradiction by dividing the system into specialized components that can operate independently.
Solution Approach 2:
The patent introduces memory-optimized data structures and intermediate representations as mediators between the CPU and hardware accelerators. These intermediaries enable efficient data transfer and reduce the overhead burden on the CPU by preprocessing and organizing data in hardware-friendly formats before acceleration.
2Adaptability or versatility
If the processor performs matrix multiplication operations for LLMs, then the LLM functionality is maintained, but the ability to perform operations for other applications is reduced
Solution Approach 1:
The system segments processing responsibilities by dedicating hardware accelerators specifically to LLM matrix multiplication operations while leaving the CPU available for other applications. This segmentation allows both LLM functionality and multi-application processing to coexist without resource contention.
Solution Approach 2:
The patent creates a multi-functional processing architecture where the CPU handles general-purpose operations across multiple applications while specialized hardware accelerators handle LLM-specific matrix multiplications. This universal design allows the system to efficiently support both LLM workloads and other applications simultaneously.
3Productivity
If hardware accelerators are used for matrix multiplication, then processing efficiency increases, but the matrix dimensions must match hardware capabilities
Solution Approach 1:
The patent implements dynamic matrix dimension adjustment by introducing padding and blocking techniques that adapt matrix sizes to match hardware accelerator capabilities. The system dynamically modifies matrix dimensions during compilation or runtime to optimize for hardware while maintaining computational correctness through appropriate padding strategies.
Solution Approach 2:
The patent changes the parameters of matrix operations by introducing padding parameters and blocking parameters that adjust matrix dimensions to match hardware accelerator requirements. These parameter transformations enable efficient hardware utilization while preserving the original computational logic through mathematically equivalent operations.
Data Source
AI summary
A processing system configured to implement a large language model (LLM) includes an accelerator unit (AU) having hardware configured to perform matrix multiplication operations for the LLM using sets of predetermined matrix dimensions. Further, to help optimize the LLM for the processing system, the processing system includes a processor that modifies one or more matrix multiplication operations of the LLM based the sets of predetermined matrix dimensions supported by the hardware of the AU. The processor then recompiles the LLM using the modified multiplication operations and implements the recompiled LLM.