首页> 外文会议>International conference on computer design >ROST-C: Reliability driven optimisation and synthesis techniques for combinational circuits
【24h】

ROST-C: Reliability driven optimisation and synthesis techniques for combinational circuits

机译:罗斯特-C:组合电路可靠性驱动优化和合成技术

获取原文

摘要

Traditional logic synthesis methodologies are driven by timing, power, and area constraints. However, due to aggressive technology shrinking and lower power requirements, circuit reliability is fast turning out to be yet another major constraint in the VLSI design flow. Soft errors, which traditionally affected only the memories, are now also resulting in logic circuit reliability degradation. In this paper, we present a systematic and integrated methodology to address and improve the combinational circuit reliability measured in terms of Soft Error Rate (SER). The proposed SER reduction framework makes use of rewriting based logic optimisation technique which employs local transformations. The main idea behind our proposal is to replace parts of the circuit with functionally equivalent but more reliable counterparts chosen from a pre-computed subset of Negation-Permutation-Negation (NPN) classes of 4-variable functions. Cut enumeration and Boolean matching driven by reliability aware optimisation algorithm are used to identify best possible replacement candidates. Our experiments on a set of MCNC benchmark circuits indicate that the proposed framework can achieve up to 75% reduction of output error probability. On average, about 14% SER reduction is obtained at the expense of very low area overhead of 6.57% that results in 13.52% higher power consumption.
机译:传统的逻辑合成方法由定时,电源和区域约束驱动。然而,由于侵略性的技术缩小和较低的功率要求,电路可靠性快速地转出了VLSI设计流程中的另一个主要约束。传统上仅影响存储器的软误差现在也导致逻辑电路可靠性降级。在本文中,我们提出了一种系统和集成的方法来解决和提高在软错误率(SER)方面测量的组合电路可靠性。所提出的SER减少框架利用基于重写的逻辑优化技术,该技术采用了本地变换。后面我们的提议的主要思想是从否定的置换否定(NPN)的4-变量函数类的预先计算的子集选择的功能上等同的但更可靠的同行更换电路的部分。由可靠性感知优化算法驱动的剪切枚举和布尔匹配用于识别最佳的替代候选者。我们在一组MCNC基准电路上的实验表明,所提出的框架可以降低输出误差概率的降低高达75%。平均而言,在非常低的面积开销的情况下获得约14%的SER减少,导致功耗更高的13.52%。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号