Hardware Matrix Decomposition Circuit Parallel Serial Segmentation
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Solution Overview
Problem
Matrix decomposition operations in computer hardware are computationally intensive, leading to high latency and power consumption due to unorganized memory access patterns and serial operation dependencies, which significantly impact performance in applications like AR and VR.
Innovation Solution
A hardware-accelerated matrix decomposition solution using specialized circuitry that splits operations into parallel and serial circuits with dependency handling for efficient execution, reducing memory bandwidth and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If matrix decomposition is implemented using general purpose processor with software kernel, then flexibility and programmability are maintained, but processing speed is slow and latency is high due to serial operation dependencies
Solution Approach 1:
The patent segments the matrix decomposition operation into independent parallel processing units. Each processing unit handles specific computational tasks (e.g., multiply-accumulate operations) independently, eliminating serial dependencies. The segmentation allows simultaneous execution of multiple operations across different processing units, directly addressing the speed-latency tradeoff by converting serial operations into parallel ones.
Solution Approach 2:
The patent introduces a hardware dimension to the computation by implementing dedicated circuitry for matrix decomposition. This transitions the problem from a software-based serial processing model to a hardware-based parallel processing model, adding spatial parallelism through multiple processing units and temporal parallelism through pipelined operations.
2Measurement precision
If matrix decomposition performs comprehensive computational operations, then accuracy and completeness of solution are improved, but power consumption increases substantially
Solution Approach 1:
The patent implements local quality by dedicating specific hardware circuits to specific computational functions within the matrix decomposition process. Each processing unit is optimized for its specific task (e.g., dedicated MAC units for multiplication-accumulation), providing high precision where needed while avoiding the overhead of general-purpose computation. This functional specialization maintains computational accuracy while reducing overall power consumption.
Solution Approach 2:
The patent replaces software-based mechanical computation with hardware-based electronic computation. Dedicated circuits perform mathematical operations directly in hardware, eliminating the need for software interpretation and sequential execution. This substitution provides both higher precision through deterministic hardware arithmetic and lower power consumption through efficient electronic operation.
3Productivity
If matrix decomposition requires substantial memory bandwidth for data access, then computational completeness is maintained, but processing latency increases due to memory access time
Solution Approach 1:
The patent implements preliminary action by pre-fetching and caching matrix data into on-chip memory buffers before computation begins. The system prepares data structures and loads necessary matrix elements into fast local memory in advance, eliminating the need for repeated slow memory accesses during the actual computation phase. This preliminary data preparation maintains computational completeness while dramatically reducing access latency.
Solution Approach 2:
The patent merges computation and memory access operations by integrating data storage buffers directly within the processing units. This co-location allows processing units to access required data elements without external memory interface overhead, effectively combining what were previously separate memory access and computation phases into a unified high-speed operation.
4Ease of manufacture
If matrix decomposition uses unorganized memory access patterns for triangular matrices, then implementation simplicity is maintained, but power consumption and latency increase
Solution Approach 1:
The patent applies asymmetry by implementing specialized memory access patterns that match the triangular structure of the matrices being processed. Rather than using generic symmetric access patterns, the system employs asymmetric access schemes that exploit the known sparsity and structure of triangular matrices, accessing only the necessary non-zero elements. This maintains implementation simplicity while dramatically reducing unnecessary memory accesses and associated energy consumption.
Data Source
AI summary
Systems and methods for a hardware accelerated matrix decomposition matrix decomposition circuit are described herein. This matrix decomposition circuit splits matrix decomposition operations into parallel operation circuits and serial operation circuits, and joins the parallel and serial operation circuits using specific dependency handling logic for efficient parallel execution. This provides fast matrix decomposition with low power consumption, reduced memory footprint, and reduced memory bandwidth.