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US 2022/0188600 A1Published application
Systems and methods for compression and acceleration of convolutional neural networks
AssigneeThe George Washington University
InventorsJiajun Li, Ahmed Louri
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Abstract
Systems and methods are disclosed for a centrosymmetric convolutional neural network (CSCNN), an algorithm/hardware co-design framework for CNN compression and acceleration that mitigates the effects of computational irregularity and effectively exploits computational reuse and sparsity for increased performance and energy efficiency.
Claims16
A centrosymmetric convolutional neural network computer system comprising:
- an input activation position buffer;
- an input activation value buffer;
- a weight value buffer;
- a weight position buffer;
- a multiplier array that receives a first vector from the input activation value buffer and a second vector from the weight value buffer and calculates a third vector of products;
- and
- a coordinate computation unit that:
- receives first associated positions of the first vector from the input activation position buffer and second associated positions of the second vector from the weight position buffer;
- calculates a fourth vector of associated positions;
- transforms the second associated positions into third associated positions, wherein the third associated positions are centrosymmetric;
- combines the second associated positions and the third associated positions into a fifth vector of associated positions.
8 more elements
A computer-implemented method of operating a centrosymmetric convolutional neural network comprising:
- receiving a first vector from an input activation value buffer and a second vector from a weight value buffer at a multiplier array;
- calculating a third vector of products at the multiplier array;
- receiving first associated positions of the first vector from the input activation position buffer and second associated positions of the second vector from the weight position buffer at a coordinate computation unit;
- calculating a fourth vector of associated positions at the coordinate computation unit;
- transforming the second associated positions into third associated positions at the coordinate computation unit, wherein the third associated positions are centrosymmetric;
- and
- combining the second associated positions and the third associated positions into a fifth vector of associated positions at the coordinate computation unit.
4 more elements
Dependencies are inferred from the claim text.