Loop Optimizer Bypassing Zero-Optimizable Instructions
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Solution Overview
Problem
Deep neural network training requires significant computational and memory resources, which exceeds the capabilities of a single commodity server, and existing methods do not efficiently handle sparse data, leading to unnecessary computations and memory consumption.
Innovation Solution
The implementation of processor and memory system extensions that identify and optimize zero-optimizable instructions, allowing for the bypass of unnecessary processing stages and efficient tracking of zero data at a cache line granularity, reducing execution cycles and storage costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional processor pipelines process all instructions uniformly, then processing completeness is maintained, but execution time increases due to unnecessary processing of zero-optimizable instructions
Solution Approach 1:
The patent extracts zero-optimizable instructions from the normal instruction stream and routes them through a specialized bypass path. The front-end extensions identify these instructions and remove them from the standard execute and writeback stages, allowing the processor to skip unnecessary processing while maintaining correctness for non-zero instructions.
Solution Approach 2:
The patent introduces front-end extensions as intermediary components between instruction fetch and execution. These extensions act as mediators that analyze instructions, identify zero-optimizable ones, and redirect them through optimized paths, thereby reducing execution time without compromising processing completeness.
2Quantity of substance
If full cache lines are allocated for sparse data, then data storage capacity is maintained, but memory resource consumption increases due to storing numerous zero values
Solution Approach 1:
The patent applies local quality by differentiating storage requirements for zero and non-zero data. Instead of uniform storage, the system uses separate zero cache lines to store only metadata about zero regions, while regular cache lines store only non-zero data values. This localized optimization reduces memory resource consumption while maintaining data storage capacity.
Solution Approach 2:
The patent segments the cache storage into distinct components: zero cache lines for tracking zero data regions and regular cache lines for non-zero data. This segmentation allows the system to manage sparse data efficiently by allocating resources only where needed, reducing overall memory consumption while preserving storage capacity.
3Productivity
If standard cache structures are used for sparse data, then cache universality is maintained, but processing efficiency decreases due to unnecessary memory access operations
Solution Approach 1:
The patent maintains cache universality by designing a hybrid cache system that can handle both dense and sparse data formats. The same cache infrastructure supports traditional storage patterns while also accommodating the optimized sparse format with zero cache lines, ensuring broad adaptability without sacrificing processing efficiency for sparse operations.
Data Source
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AI summary
Loop code processor optimizations are implemented as a loop optimizer extension to a processor pipeline. The loop optimizer generates optimized code associated with code loops that include at least one zero-optimizable instruction. The loop optimizer may generate multiple versions of optimized code associated with a particular code loop, where each of the multiple version of optimized code has a different associated condition under which the optimized code can be safely executed.