Macro-Op Fusion Prediction to Limit Pipeline Flushes
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Solution Overview
Problem
Existing macro-op fusion techniques in RISC-V architectures fail to capitalize on all potential efficiency gains due to limited consideration of fusion opportunities, leading to suboptimal performance and increased instruction set complexity.
Innovation Solution
Implement a dynamic fusion predictor to determine whether to fuse macro-ops based on the likelihood of completing a sequence, using a table of prediction counters and hysteresis to optimize execution, and support pipeline flushes for mispredictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If macro-op fusion is implemented in RISC-V architectures, then processor performance and efficiency are improved, but instruction set complexity increases
Solution Approach 1:
The instruction set is segmented into base RISC-V instructions and fused macro-ops. The fusion mechanism is implemented as a separate layer that combines multiple base instructions into compound operations without modifying the core ISA, thereby improving performance while containing complexity in a modular fashion
Solution Approach 2:
A fusion prediction mechanism acts as an intermediary between the instruction fetch stage and execution stage. This mediator determines whether to fuse incoming instructions based on prediction counters and hysteresis logic, enabling performance optimization without permanently complicating the instruction set architecture
2Productivity
If dynamic fusion prediction is used to optimize macro-op fusion, then execution efficiency is improved, but circuit area increases
Solution Approach 1:
The fusion prediction mechanism uses partial action by only fusing instructions when prediction counters indicate a high likelihood of success. The hysteresis logic ensures fusion occurs only when benefits clearly outweigh costs, avoiding unnecessary fusion operations that would waste circuit resources
Solution Approach 2:
The prediction counters use adjustable threshold parameters and hysteresis values that can be tuned to optimize the balance between fusion aggressiveness and circuit resource usage. By changing these parameters, the system adapts fusion behavior to match available circuit area while maintaining execution efficiency
3Quantity of substance
If macro-op fusion sequences are implemented, then instruction count is reduced, but pipeline flush operations increase
Solution Approach 1:
The system performs preliminary validation during the decode stage to ensure that instructions selected for fusion are guaranteed to complete their sequences without requiring pipeline flushes. This preliminary check prevents fusion of instruction sequences that would later cause pipeline disruptions, reducing overall flush operations
Solution Approach 2:
The prediction counter mechanism incorporates feedback from actual fusion outcomes to refine future fusion decisions. By learning from past fusion successes and failures, the system optimizes which instructions to fuse in a way that minimizes pipeline flush operations while maintaining reduced instruction count benefits
Data Source
AI summary
Systems and methods are disclosed for macro-op fusion in pipelined architectures. For example, some methods include detecting a sequence of macro-ops stored in an instruction decode buffer, the sequence of macro-ops including a first macro-op, followed by one or more intervening macro-ops, followed by a last macro-op; determining a micro-op that is equivalent to the first macro-op combined with the last macro-op; and forwarding the micro-op to one or more execution resource circuitries for execution.