指令扩展中相关子图的分析与处理
Analyzing and Processing of Related Subgraphs in Instruction-Set Extension
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摘要: 由于没有充分考虑候选子图间重叠、包含等相关关系,按照现有指令扩展方法选出的扩展指令可能无法带来预期的性能收益.详细讨论了子图相关关系和处理方法,并提出相应的指令扩展算法.在子图识别时,根据延迟约束合并相互包含的子图,根据重叠结点的位置合并重叠子图,缩小搜索空间;在子图压缩时,根据子图相关关系动态地调整候选子图的性能收益,减少相关子图引起的估算误差.此外,还根据剩余候选子图的性能收益对时间与精度进行折中.该算法已在传输触发结构ASIP的自动设计流程中实现,实际性能加速比与预期值的平均误差仅为0.17%,远低于原先的3.6%.Abstract: Because of lacking candidate subgraphs relationship analysis,extended instructions generated by current instruction extension method may not be able to obtain expectable performance improvement.Subgraph relationships are discussed in detail and corresponding instruction set extension algorithm is proposed.During subgraph identification,related subgraphs are combined together under latency constraint and overlapped subgraphs are combined according to the position of overlapped nodes.During subgraph compaction,the performance gain can be adjusted dynamically according to subgraph relationships.Both can eliminate estimation errors effectively.Moreover,this algorithm also makes tradeoff between time cost and precision according to performance gain of left candidate subgraphs.This method has already been implemented in an automatic design flow for TTA based ASIP,with only 0.17% estimation errors on average,far less than the original 3.6%.
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