Pipelined Pointwise Convolution Hardware Architecture
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Solution Overview
Problem
Existing hardware solutions struggle to efficiently perform 3D convolution operations for complex artificial intelligence tasks without introducing significant complexity and resource constraints.
Innovation Solution
A processor system is designed to decompose 3D convolution operations into separable depthwise and pointwise operations, where a hardware channel convolution processor unit performs depthwise convolutions and a hardware dot product processor unit performs pointwise convolutions, optimizing the pipeline and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional 3D convolution operations are performed using general-purpose processors, then hardware compatibility is maintained, but performance and computational efficiency are insufficient
Solution Approach 1:
The patent segments the 3D convolution operation into two distinct stages: depthwise convolution and pointwise convolution. This segmentation allows each stage to be optimized independently with specialized hardware units, improving overall performance without requiring a completely complex redesign of the entire system.
Solution Approach 2:
The patent implements a dynamic pipeline architecture where the depthwise convolution unit and pointwise convolution unit operate in sequence, with results being passed between stages. This dynamic pipeline allows for continuous processing and optimization of each stage independently, enhancing productivity while managing complexity.
2Productivity
If specialized hardware is used to optimize convolution operations, then computational efficiency improves, but device complexity and memory transformation requirements increase
Solution Approach 1:
By separating depthwise and pointwise convolutions into distinct hardware units, the patent simplifies the memory organization requirements for each unit. Each unit can be optimized with simpler, dedicated memory structures rather than requiring complex universal memory transformation schemes.
Solution Approach 2:
The patent introduces an intermediary pipeline stage between depthwise and pointwise convolutions, where intermediate results are stored and transferred. This intermediary structure simplifies memory management by providing a clear buffer zone between the two specialized units, reducing the overall memory transformation complexity.
3Productivity
If traditional convolution methods are used, then hardware compatibility is maintained, but resource utilization and performance significantly decrease
Solution Approach 1:
The patent creates a dynamic hardware architecture where the depthwise convolution unit and pointwise convolution unit operate in a pipelined manner, allowing for continuous data flow and improved resource utilization. This dynamic structure enables better hardware resource usage compared to static traditional methods.
Solution Approach 2:
The patent performs depthwise convolution operations first as a preliminary step, producing intermediate results that are then used by the pointwise convolution unit. This preliminary action optimizes the data flow and resource utilization across the hardware architecture, improving overall productivity.
Data Source
AI summary
A processor system comprises a hardware channel convolution processor unit and dot product processor unit. The channel convolution processor unit is configured to perform depthwise convolution, including by multiplying each data element of a first group of data elements of a convolution data matrix with a corresponding data element of a second group of data elements of a plurality of depthwise convolution weight matrices and summing together, for each specific channel, multiplication results corresponding to the specific channel to determine one corresponding result data element in a corresponding channel convolution result matrix to calculate a portion of depthwise convolution results. The dot product processor unit is configured to perform pointwise convolution, including applying pointwise weight matrices to the portion of depthwise convolution results to determine a portion of separable convolution results while at least another portion of the depthwise convolution results is being calculated by the processor system.