Sumeet Katariya
University of Wisconsin-Madison
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Featured researches published by Sumeet Katariya.
international conference on computer design | 2010
Shi-Ting Zhou; Sumeet Katariya; Hamid Reza Ghasemi; Stark C. Draper; Nam Sung Kim
The increasing power consumption of processors has made power reduction a first-order priority in their design. Voltage scaling is one of the most successful power-reduction techniques introduced to date, but it is limited to some minimum voltage, VDDMIN, below which all components cannot operate reliably. In particular, ever-increasing process variability due to shrinking feature size further degrades the low-voltage reliability of, e.g., SRAM cells. Larger SRAM cells are less sensitive to process variability and their use would allow a reduction in VDDMIN. However, large-scale memory structures, e.g., last-level caches (LLCs) that often determine the VDDMIN of processors, cannot afford to use such large SRAM cells due to the resulting increase in die area. In this paper we propose a joint optimization of LLC cell size, number of redundant cells, and ECC (error-correction coding) strength to minimize total SRAM area while meeting target yields and VDDMIN. The use of redundant cells and ECC enable the use of smaller cell sizes while maintaining target yields and VDDMIN. Smaller cell sizes more than make up for the extra cells required by redundancy and ECC. We first assess each approach individually, i.e., only redundancy or ECC for various cell sizes. We then consider a combined approach and observe significant improvements. For example, in 32nm technology our combined approach yields a 27% reduction in total SRAM area (including redundant cells) when targeting a VDDMIN of 600mV.
IEEE Transactions on Very Large Scale Integration Systems | 2012
Nam Sung Kim; Stark C. Draper; Shi Ting Zhou; Sumeet Katariya; Hamid Reza Ghasemi; Taejoon Park
The increasing power consumption of processors has made power reduction a first-order priority in processor design. Voltage scaling is one of the most powerful power-reduction techniques introduced to date, but is limited to some minimum voltage VDDMIN. Below VDDMIN on-chip SRAM cells cannot all operate reliably due to increased process variability with technology scaling. The use of larger SRAM cells, which are less sensitive to process variability, allows a reduction in VDDMIN. However, since the large-scale memory structures such as last-level caches (LLCs) often determine the VDDMIN of processors, these structures cannot afford to use large SRAM cells due to the resulting increase in die area. In this paper we first propose a joint optimization of LLC cell size, the number of redundant cells, and the strength of error-correction coding (ECC) to minimize total SRAM area while meeting yield and VDDMIN targets. The joint use of redundant cells and ECC enables the use of smaller cell sizes while maintaining design targets. Smaller cell sizes more than make up for the extra cells required by redundancy and ECC. In 32-nm technology our joint approach yields a 27% reduction in total SRAM area (including the extra cells) when targeting 90% yield and 600 mV VDDMIN. Second, we demonstrate that the ECC used to repair defective cells can be combined with a simple architectural technique, which can also fix particle-induced soft errors, without increasing ECC strength or processor runtime.
international conference on artificial intelligence and statistics | 2015
Kevin G. Jamieson; Sumeet Katariya; Atul Deshpande; Robert D. Nowak
Archive | 2009
Udayan Kanade; Pushkar Apte; Ruby Rama Praveen; Sanat Ganu; Sumeet Katariya; Alok Deshpande; Parag Khairnar
international conference on machine learning | 2016
Sumeet Katariya; Branislav Kveton; Csaba Szepesvári; Zheng Wen
Archive | 2009
Udayan Kanade; Pushkar Apte; Ruby Rama Praveen; Sanat Ganu; Sumeet Katariya; Alok Deshpande; Parag Khairnar
international conference on artificial intelligence and statistics | 2017
Sumeet Katariya; Branislav Kveton; Csaba Szepesvári; Claire Vernade; Zheng Wen
international joint conference on artificial intelligence | 2017
Sumeet Katariya; Branislav Kveton; Csaba Szepesvári; Claire Vernade; Zheng Wen
Archive | 2009
Udayan Kanade; Pushkar Apte; Ruby Rama Praveen; Sanat Ganu; Sumeet Katariya; Alok Deshpande; Parag Khairnar
Archive | 2009
Udayan Kanade; Pushkar Apte; Ruby Rama Praveen; Sanat Ganu; Sumeet Katariya; Alok Deshpande; Parag Khairnar