GPU ReLU Activation Early Exit for Lower Compute Overhead
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Solution Overview
Problem
Existing graphics processing units (GPUs) face inefficiencies in processing neural network operations, particularly with the ReLU activation function, due to the need for separate operations on positive and zero outputs, which can hinder performance and increase computational overhead.
Innovation Solution
Implementing an early exit mechanism for the ReLU activation function, allowing the GPU to skip zero outputs and perform computations only on positive inputs, thereby optimizing processing efficiency and reducing unnecessary operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If separate operations are performed on positive and zero outputs for ReLU activation, then computational accuracy is maintained, but processing speed decreases and computational overhead increases
Solution Approach 1:
The patent extracts the zero-output cases from the general ReLU processing pipeline and handles them separately through an early exit mechanism. When the input is determined to be negative (resulting in zero output), the system exits the computation early without performing unnecessary operations on the zero output, thereby reducing computational overhead while maintaining accuracy.
Solution Approach 2:
The patent implements a mechanism to skip redundant computations for zero outputs. By detecting negative inputs early in the processing pipeline, the system rushes through the ReLU activation by directly outputting zero without executing subsequent operations that would be wasted on zero values, thus improving processing speed.
2Use of energy by moving object
If redundant computations are performed on zero outputs, then complete processing coverage is ensured, but power consumption increases
Solution Approach 1:
The patent extracts and identifies zero-output cases through early detection of negative inputs. By separating these cases from the main processing flow, the system avoids performing redundant computations that would consume unnecessary power, thereby improving energy efficiency without compromising processing completeness.
Solution Approach 2:
The ReLU activation function itself provides the optimization by leveraging its inherent property that negative inputs naturally produce zero outputs. The system uses this self-service characteristic to automatically identify and exit early for negative inputs, reducing power consumption without requiring external intervention or complex additional mechanisms.
Data Source
AI summary
One embodiment provides a graphics processor comprising a memory interface and a processing resource coupled with the memory interface. The processing resource including circuitry configured to perform an operation fused with a rectified linear unit operation. The circuitry is configured to detect a negative output of the operation before completion of the operation, clock gate a portion of the circuitry, and output a zero value for the rectified linear unit operation.