Hardware Loop Memoization for CPU Throughput
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Solution Overview
Problem
Existing software-based memoization techniques for loops are conservative and miss significant performance opportunities, while hardware-based approaches have limited benefits due to inefficiencies in detecting and eliminating repeating loop sequences.
Innovation Solution
A hardware-based loop memoization technique that uses a path-based predictor to identify repeating loop sequences, eliminates instructions from the pipeline, and provides output values from a memoization table, allowing dependent instructions to execute earlier, thereby enhancing performance and power efficiency without requiring software changes or ISA additions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If software-based memoization techniques are used, then implementation simplicity is maintained, but performance improvement is limited due to conservative detection and overhead
Solution Approach 1:
The patent replaces software-based memoization with a hardware-based system that uses dedicated circuits (memoization table, path-based predictor) to detect and eliminate repeating loop sequences. This substitution of mechanical/software systems with hardware systems resolves the contradiction by providing aggressive loop detection without software overhead, achieving high performance improvement while managing complexity through specialized hardware components.
Solution Approach 2:
The patent introduces a path-based predictor as an intermediary component that bridges the instruction pipeline and the memoization table. This intermediary detects repeating loop sequences by analyzing path information and coordinates with the memoization table to eliminate redundant executions, thereby achieving high productivity while distributing complexity across multiple specialized components rather than a single complex software system.
2Productivity
If hardware-based loop memoization is implemented, then performance improvement is enhanced, but detection and elimination efficiency is reduced due to existing hardware approach inefficiencies
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing loop output results in the memoization table before they are needed. When a repeating loop sequence is detected by the path-based predictor, the system can immediately retrieve pre-computed results instead of re-executing the loop, thereby eliminating detection and elimination time while maintaining high productivity.
Solution Approach 2:
The patent implements skipping by allowing the instruction pipeline to bypass the execution of repeating loop sequences entirely. Once the path-based predictor identifies a repeating loop pattern, it signals the memoization table to provide results, and the pipeline skips directly to the next independent instruction, effectively rushing through the redundant computation and minimizing loss of time.
3Use of energy by moving object
If loop instructions are eliminated from the pipeline, then CPU cycles are reduced, but pipeline flushing overhead increases on misprediction
Solution Approach 1:
The patent implements feedback through the path-based predictor that continuously monitors loop execution patterns and provides feedback to the memoization system. When repeating patterns are detected, the system activates memoization; when patterns change, it disables memoization and flushes the pipeline. This feedback mechanism ensures that pipeline flushing occurs only when necessary, minimizing energy loss while maximizing CPU cycle reduction through selective loop elimination.
Solution Approach 2:
The patent applies dynamics by making the memoization mechanism adaptive rather than static. The path-based predictor dynamically adjusts which loops are memoized based on runtime behavior analysis, and the system dynamically flushes the pipeline when loop patterns change. This dynamic approach ensures that CPU cycles are reduced for repeating patterns while pipeline flush energy is minimized by only flushing when actual changes occur, rather than using a fixed conservative approach.
Data Source
AI summary
Methods and apparatus for instruction elimination through hardware driven memoization of loop instances. A hardware-based loop memoization technique learns repeating sequences of loops and transparently removes instructions for the loop instructions from instruction sequences while making their output available to dependent instructions as if the loop instructions had been executed. A path-based predictor is implemented at the front-end to predict these loop instances and remove their instructions from instruction sequences. A novel memoization prediction micro-operation (Uop) is inserted into the instruction sequence for instances of loops that are predicted to be memoized. The memoization prediction Uop is used to compare the input signature (expected set of input values for the loop) with the actual signature to determine correct and incorrect predictions. The input signature learnt is based on all live-ins of a loop, both explicit register-based live-ins as well as loads to memory in the loop body that determine code path and outputs.