Variation in performance and power across manufactured parts and their operating conditions is an accepted reality in aggressive CMOS processes. This paper considers challenges and opportunities in identifying this variation and methods to combat it for improved computing systems. We introduce the notion of instruction-level vulnerability (ILV) to expose variation and its effects to the software stack for use in architectural/compiler optimizations. To compute ILV, we quantify the effect of voltage and temperature variations on the performance and power of a 32-bit, RISC, in-order processor in 65nm TSMC technology at the level of individual instructions. Results show 3.4ns (68FO4) delay variation and 26.7x power variation among instructions, and across extreme corners. Our analysis shows that ILV is not uniform across the instruction set. In fact, ILV data partitions instructions into three equivalence classes. Based on this classification, we show how a low-overhead robustness enhancement techniques can be used to enhance performance by a factor of 1.1x−5.5x.
Rahimi A., Benini L. , Gupta R.K. (2012). Analysis of instruction-level vulnerability to dynamic voltage and temperature variations. NEW YORK : IEEE Press [10.1109/DATE.2012.6176659].
Analysis of instruction-level vulnerability to dynamic voltage and temperature variations
BENINI, LUCA;
2012
Abstract
Variation in performance and power across manufactured parts and their operating conditions is an accepted reality in aggressive CMOS processes. This paper considers challenges and opportunities in identifying this variation and methods to combat it for improved computing systems. We introduce the notion of instruction-level vulnerability (ILV) to expose variation and its effects to the software stack for use in architectural/compiler optimizations. To compute ILV, we quantify the effect of voltage and temperature variations on the performance and power of a 32-bit, RISC, in-order processor in 65nm TSMC technology at the level of individual instructions. Results show 3.4ns (68FO4) delay variation and 26.7x power variation among instructions, and across extreme corners. Our analysis shows that ILV is not uniform across the instruction set. In fact, ILV data partitions instructions into three equivalence classes. Based on this classification, we show how a low-overhead robustness enhancement techniques can be used to enhance performance by a factor of 1.1x−5.5x.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
