Tensor Contraction Architecture for Higher-Rank Tensors
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Solution Overview
Problem
Current systems are unable to efficiently perform tensor contractions on higher rank tensors due to increased size and energy requirements, limiting their application in AI and machine learning tasks.
Innovation Solution
A system comprising a processing unit, memory, and programmable logic with an input data arbitrator, tensor contraction block, and output data arbitrator, which routes and processes tensors by flattening, interleaving, and reconfiguring them to perform matrix multiplication operations, allowing for tensor contractions of rank N tensors by reducing them into arrays of rank 2 tensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current systems are used to perform tensor contractions on higher rank tensors, then tensor contraction capability is improved, but system size and energy requirements increase dramatically
Solution Approach 1:
The patent segments higher rank tensors into multiple rank-2 tensors and performs contractions using a network of arrays of processing elements. Each array processes specific tensor components independently, enabling higher rank tensor operations without requiring a monolithic system that would consume excessive energy.
Solution Approach 2:
The patent transforms higher rank tensor operations into equivalent rank-2 tensor operations by flattening and reorganizing tensor data across multiple arrays. This dimensional transformation allows the system to handle higher rank tensors using standard matrix multiplication hardware, avoiding the need for specialized high-energy-consuming processors.
2Adaptability or versatility
If current systems are used to perform tensor contractions on higher rank tensors, then tensor contraction capability is improved, but system size increases dramatically
Solution Approach 1:
The system divides the tensor contraction task across multiple arrays of processing elements, where each array handles specific tensor components. This segmentation allows the system to process higher rank tensors using distributed, compact arrays rather than requiring a single large processing unit.
Solution Approach 2:
The patent creates a universal tensor contraction system where arrays of processing elements can handle both rank-2 and higher rank tensors through standardized operations. The input data arbitrator and output data arbitrator provide universal interfaces that work across different tensor ranks, eliminating the need for specialized hardware for each tensor type.
3Productivity
If tensors are flattened and interleaved for processing, then processing efficiency is improved, but data transformation complexity increases
Solution Approach 1:
The processing unit performs preliminary flattening and interleaving of tensor data before it reaches the tensor contraction block. By pre-processing the data into the correct format, the system eliminates the need for complex real-time transformations during the actual contraction operation, improving overall processing efficiency.
Solution Approach 2:
The input data arbitrator acts as an intermediary that manages the complex data transformation process. It receives higher rank tensors, coordinates the flattening and interleaving operations, and delivers properly formatted data to the processing arrays, isolating the complexity from the core computation engine.
Data Source
AI summary
Systems and methods for performing tensor contractions are provided. The system includes a processing system and a programmable logic in communication with the processing system via a controller. The processing system includes a processing unit and a memory for storing tensors. The programmable logic includes an input data arbitrator for routing a first input tensor and a second input tensor from the controller to a tensor contraction block; the tensor contraction block that includes a network of arrays of processing elements for performing matrix multiplication operations on the first and second input tensor; and an output data arbitrator for routing an output of the tensor contraction block to the processing system. The network of arrays of processing elements may include N arrays of processing elements, where N corresponds to the rank of the output tensor.