Statically Reconfigurable Dataflow Processor for N-Dimensional Affine Transformations
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Solution Overview
Problem
Conventional CPUs and GPUs incur significant overhead due to dynamic reconfiguration and implicit instruction dependencies while performing affine transformations on images, which hampers efficiency in training neural networks and other applications requiring large numbers of image transformations.
Innovation Solution
A statically reconfigurable dataflow architecture processor (SRDAP) that maps an N-dimensional image affine transformation computation graph statically onto hardware, eliminating the need for dynamic instruction fetching and scheduling, and providing direct communication between dataflow pipelines to enhance throughput and reduce dependencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional CPUs and GPUs are used to perform affine transformations, then the processor can handle various types of transformations through dynamic reconfiguration, but significant overhead is incurred due to dynamic instruction fetching and scheduling
Solution Approach 1:
The patent applies dynamics by enabling the dataflow processor to be reconfigured between operations to handle different affine transformation types (rotation, scaling, shearing, translation). The processor transitions from a static fixed-function design to a dynamically reconfigurable architecture where computation graphs can be loaded and unloaded, allowing versatility while maintaining efficiency for each specific transformation type.
Solution Approach 2:
The patent segments the affine transformation computation into distinct computational stages represented as computation graphs. Each transformation type is broken down into separate nodes (coordinate transformation, interpolation, pixel value calculation) that can be independently optimized and executed in a dataflow manner, reducing overhead by eliminating sequential instruction fetching.
2Adaptability or versatility
If dynamic reconfiguration is used to perform different affine transformations, then the processor can adapt to various transformation types, but implicit instruction dependencies increase overhead and reduce efficiency
Solution Approach 1:
The patent replaces the mechanical instruction-fetching and scheduling system of conventional CPUs with a dataflow-based execution model. Instead of sequentially fetching and decoding instructions, the system loads computation graphs that define data dependencies explicitly, allowing operations to execute as soon as their input data is available, thereby eliminating instruction scheduling complexity.
Solution Approach 2:
The patent changes the fundamental execution parameter from sequential instruction-based control to data-driven parallel execution. By representing transformations as computation graphs with explicit data dependencies rather than implicit instruction dependencies, the system allows flexible adaptation to different transformation types without the overhead of instruction scheduling and dependency resolution.
3Measurement precision
If a large library of samples is collected for neural network training, then prediction accuracy improves, but time and expense increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-processing and transforming a small set of training images through various affine transformations (rotations, scalings, shearing, translations) to generate a large expanded training library. This allows the system to prepare diverse training samples in advance, improving neural network prediction accuracy without requiring manual collection of millions of images, thereby significantly reducing time and expense.
4Manufacturing precision
If affine transformations are performed sequentially on individual images, then each transformation can be processed accurately, but the overall processing speed is insufficient for large-scale applications
Solution Approach 1:
The patent implements continuity of useful action by organizing computations into dataflow pipelines where multiple images can be processed simultaneously through overlapping computation stages. While one image is being transformed at the coordinate level, another image can be undergoing interpolation, and a third can be having pixel values calculated, thereby maintaining continuous productive action across the system without sacrificing individual transformation accuracy.
Solution Approach 2:
The patent transitions from sequential single-image processing to parallel multi-image processing by adding the dimension of parallelism through dataflow architecture. Multiple computation graphs representing different images can be loaded and executed concurrently, with the dataflow scheduler managing resource allocation across images, thereby dramatically increasing throughput while maintaining the precision of individual affine transformations.
Data Source
AI summary
A statically reconfigurable dataflow architecture processor performs an N-dimensional affine transform specified by a matrix on an input image to produce an output image. N counters iterate over the output image by N respective stride values (N output tile dimension lengths) to generate base pixel coordinates of N-dimensional output tiles into which the output image is subdividable. Statically reconfigurable pattern compute units, for each output tile of the output tiles: use the base pixel coordinates of the output tile and the N output tile dimension lengths to calculate the coordinates of corner pixels of the output tile and apply the affine transform matrix to the corner pixel coordinates and use the results to determine base pixel coordinates of a corresponding N-dimensional input tile into which the input image is subdividable. Statically reconfigurable pattern memory units load each corresponding input tile based on the determined input tile base pixel coordinates.