Processor Pipeline Extensions for Sparse Data Processing
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Solution Overview
Problem
Deep neural networks require significant computational and memory resources for training, which exceeds the capabilities of a single commodity server, especially due to the inefficiencies in processing and storing sparse data with many zero values.
Innovation Solution
Implementing processor and memory system extensions that utilize zero-optimizable instructions to bypass unnecessary processing stages and efficiently track zero data at a cache line granularity, reducing execution cycles and storage costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional processor pipelines process all instructions through execute and writeback stages, then complete instruction processing is achieved, but computational resources are wasted on zero-optimizable instructions
Solution Approach 1:
The patent extracts zero-optimizable instructions from the normal instruction processing flow by adding front-end extensions that detect these instructions and route them through a simplified path that bypasses the execute and writeback stages. This separation allows the processor to handle zero-optimizable instructions more efficiently while maintaining normal processing for other instructions.
Solution Approach 2:
The processor pipeline is segmented into different processing paths: a full path for regular instructions (fetch, decode, execute, writeback, commit) and a simplified path for zero-optimizable instructions (fetch, decode, commit). This segmentation enables selective optimization based on instruction type without compromising the完整性 of standard instruction processing.
2Quantity of substance
If dense cache structures store all cache lines with full tags, then cache coverage is maximized, but memory resources are wasted on zero cache lines
Solution Approach 1:
The patent applies local quality by creating a specialized zero cache with a compact tag-only structure specifically for storing zero cache lines, while the main data cache continues to store non-zero cache lines with full tags and data. This localized optimization reduces memory usage for zero cache lines without affecting the functionality of the main cache for non-zero data.
Solution Approach 2:
Instead of storing full cache line data for zero cache lines as in traditional caches, the patent inverts the approach by storing only the cache tags in a separate zero cache structure. The actual zero data is implicitly represented, eliminating the need to allocate memory for storing redundant zero values while maintaining cache functionality.
3Reliability
If processor pipelines process all instructions through complete stages, then instruction accuracy is ensured, but execution time increases for zero-optimizable instructions
Solution Approach 1:
The patent implements preliminary action by detecting zero-optimizable instructions in the front-end of the pipeline before they reach the execute stage. The detection and routing decision is made in advance, allowing the instruction to be committed directly without undergoing unnecessary execute and writeback stages, thus reducing execution time while maintaining correctness through proper detection logic.
Data Source
AI summary
Efficient instruction processing for sparse data includes extensions to a processor pipeline to identify zero-optimizable instructions that include at least one zero input operand, and bypass the execute stage of the processor pipeline, determining the result of the operation without executing the instruction. When possible, the extensions also bypass the writeback stage of the processor pipeline.