Static Dataflow Affine Transform Processor Without Instruction Overhead
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional CPUs and GPUs incur significant overhead due to dynamic reconfiguration and implicit instruction dependencies when performing affine transformations on images, which affects the efficiency and feasibility of training neural networks and other applications requiring numerous transformations.
Innovation Solution
A statically reconfigurable dataflow architecture processor (SRDAP) performs N-dimensional affine transformations by statically configuring its datapath and memory using a dataflow program, eliminating the need for dynamic instruction fetching and handling of implicit dependencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional CPUs and GPUs are used to perform affine transformations dynamically, then flexibility and adaptability are maintained, but significant overhead is incurred due to dynamic reconfiguration and implicit instruction dependencies
Solution Approach 1:
The patent applies static reconfiguration instead of dynamic reconfiguration. The processor is configured for a specific affine transformation type (rotation, scaling, shearing, etc.) before operation, eliminating the need for runtime reconfiguration. This resolves the contradiction by sacrificing adaptability (can only perform pre-configured transformation types) to dramatically reduce overhead and complexity.
2Adaptability or versatility
If conventional processors use dynamic instruction fetching and execution, then general-purpose computing is enabled, but efficiency is reduced due to implicit instruction dependencies and scheduling overhead
Solution Approach 1:
The patent segments the affine transformation computation into distinct functional units: coordinate transformation units, address generation units, and data retrieval units. Each unit performs a specific sub-task in parallel, eliminating instruction dependencies and scheduling overhead. This segmentation enables high-efficiency execution of affine transformations while maintaining sufficient versatility through configurable parameters.
Solution Approach 2:
The patent replaces the mechanical instruction-fetching and execution system with a dataflow-based computational system. Instead of sequentially fetching and decoding instructions, the system directly computes transformed coordinates and retrieves pixel data based on pre-configured transformation parameters, eliminating the overhead of the instruction execution mechanism.
3Reliability
If numerous affine transformations are performed for neural network training, then training accuracy is improved, but the time and computational resources required become prohibitive
Solution Approach 1:
The patent performs preliminary configuration of the processor for specific affine transformation types (rotation angles, scaling factors, shearing parameters) before the actual transformation operations. This pre-configuration enables the processor to execute multiple transformations of the same type at high speed without reconfiguration overhead, making large-scale neural network training feasible by reducing the time penalty for each transformation.
Data Source
AI summary
A statically reconfigurable dataflow architecture processor (SRDAP) performs an N-dimensional affine transform specified by a matrix on an input image to produce an output image includes pattern compute units (PCUs) and pattern memory units (PMUs) interconnected by switches. PCUs have vector pipelines of functional units that perform operations on operands received from previous pipeline stages, another PCU, and/or PMUs. PMUs have memories loadable with the input image. The PCUs and PMUs are statically reconfigurable to, for all the output pixels: apply the matrix to vectors of output pixel coordinates to calculate corresponding vectors of input pixel coordinates, flatten the vectors of input pixel coordinates into vectors of PMU addresses of the input pixels, read values of the input pixels from the PMUs at the calculated input pixel addresses, and write vectors of the input pixel values to PMUs to form the output image.