Neural Network Training Chip With 2D Vector Array and Matrix Multiply
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Solution Overview
Problem
Existing neural network training methods are inefficient and not optimized for high-dimensional tensor computations and 32-bit precision, limiting performance in machine learning applications.
Innovation Solution
A special-purpose hardware chip designed with a scalar processor, vector processor, matrix multiply unit, and sparse computation core, optimized for high-dimensional tensor operations and 32-bit precision, featuring a 2D array of vector processing units and high-bandwidth memory, enabling efficient neural network training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If general-purpose processors are used for neural network training, then programming flexibility is maintained, but computational efficiency and performance for high-dimensional tensor operations deteriorate
Solution Approach 1:
The processor is divided into multiple vector processing units organized in a two-dimensional array, where each unit can independently execute vector operations. This segmentation enables parallel processing of high-dimensional tensors while maintaining manageable complexity through modular design
Solution Approach 2:
The invention transitions from traditional scalar or one-dimensional vector processing to two-dimensional array-based vector processing units. This dimensional change enables efficient handling of high-dimensional tensors (2D matrices, 3D tensors, etc.) native to machine learning workloads, significantly improving computational efficiency for neural network training
2Productivity
If standard precision computations are used, then compatibility with existing software is maintained, but performance for 32-bit precision machine learning computations deteriorates
Solution Approach 1:
The vector processing units are specifically optimized with 32-bit precision arithmetic capabilities tailored for machine learning workloads. This local optimization allows the system to achieve high performance for 32-bit computations while maintaining compatibility with other precision formats through software abstraction layers
3Productivity
If memory bandwidth is not optimized, then system cost is reduced, but performance for high-dimensional tensor operations deteriorates
Solution Approach 1:
The memory system is integrated directly with the vector processing units, with each vector processing unit having access to dedicated memory resources. This merging of computation and memory resources eliminates the memory wall bottleneck, enabling high-bandwidth access for high-dimensional tensor operations without requiring complex external memory subsystems
Data Source
AI summary
Methods, systems, and apparatus including a special purpose hardware chip for training neural networks are described. The special-purpose hardware chip may include a scalar processor configured to control computational operation of the special-purpose hardware chip. The chip may also include a vector processor configured to have a 2-dimensional array of vector processing units which all execute the same instruction in a single instruction, multiple-data manner and communicate with each other through load and store instructions of the vector processor. The chip may additionally include a matrix multiply unit that is coupled to the vector processor configured to multiply at least one two-dimensional matrix with a second one-dimensional vector or two-dimensional matrix in order to obtain a multiplication result.