PPT-ChargeCache Reducing DRAM Latency by Exploiting Row

Author : briana-ranney | Published Date : 2018-11-09

Access Locality Hasan Hassan Gennady Pekhimenko Nandita Vijaykumar Vivek Seshadri Donghyuk Lee Oguz Ergin Onur Mutlu Executive Summary Goal Reduce average

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ChargeCache Reducing DRAM Latency by Exploiting Row: Transcript

Access Locality Hasan Hassan Gennady Pekhimenko Nandita Vijaykumar Vivek Seshadri Donghyuk Lee Oguz Ergin Onur Mutlu Executive Summary Goal Reduce average DRAM access latency with no modification to the existing DRAM chips. Niladrish. . Chatterjee. Manjunath. . Shevgoor. Rajeev . Balasubramonian. Al Davis. Zhen Fang. ‡†. Ramesh . Illikkal. *. Ravi . Iyer. *. University of Utah , NVidia. ‡. and Intel Labs*. †. K. . Qureshi. ECE, Georgia Tech. Gabriel H. Loh, AMD. Fundamental Latency Trade-offs. in Architecting DRAM Caches. MICRO 2012. 3-D Memory Stacking. 3-D Stacked memory can provide large caches at high . Bank Privatization for Predictability and Temporal Isolation. Sungjun. . Kim . Columbia . University. Edward A. Lee . UC . Berkeley . Isaac . Liu . UC Berkeley. Hiren. D. Patel University of Waterloo. DRAM Scaling. Prof. Onur Mutlu. http://www.ece.cmu.edu/~omutlu. onur@cmu.edu. HiPEAC. ACACES Summer School 2013. July 15-19, . 2013. The Main Memory System. Main memory is a critical component of all computing systems. OLTP on NVM:. YMMV. The Last Six Months. ?. PDL Retreat. October 2013. PDL Visit Day. May 2014. Prison Life. Washing Dishes. Not Fighting. Repentant. Cafeteria Thievery. Shankings. Making . Pruno. GOOD. OLTP on the NVM SDV:. YMMV. 2013. January. Retreat. Thesis. Defense. December. Retreat. Job. Interviews. Moved to. CMU. 2013. January. Retreat. Thesis. Defense. December. Retreat. Job. Interviews. Moved to. K. . Qureshi. ECE, Georgia Tech. Gabriel H. Loh, AMD. Fundamental Latency Trade-offs. in Architecting DRAM Caches. MICRO 2012. 3-D Memory Stacking. 3-D Stacked memory can provide large caches at high . goals for taxonomy session. survey sources of latency. categorise solutions. quantify benefits. consider deployment aspects. short-term & long-term applicability. common reference framework for discussions. ISCA 2012 . Michele . Franceschini. , . Ashish. . Jagmohan. , Luis . Lastras. . IBM T. J. Watson Research Center. PreSET. : . Improving PCM performance. b. y exploiting asymmetry in write times. Niladrish Chatterjee. Mike O’Connor. Gabriel H. . Loh. Nuwan. . Jayasena. Rajeev . Balasubramonian. Irregular GPGPU Applications. Conventional GPGPU workloads access vector or matrix-based data structures. by Exploiting the Variation in Local . Bitlines. . Jeremie S. Kim. . Minesh. Patel . Hasan Hassan . Onur. . Mutlu. . Motivation. : DRAM latency is a . major performance bottleneck. Problem. Computer Architecture Lecture 6b: SoftMC Hasan Ibrahim Hasan ETH Zürich Fall 2018 4 October 2018 SoftMC A Flexible and Practical Open-Source Infrastructure for Enabling Experimental DRAM Studies Charge-Level-Aware Look-Ahead Partial Restoration. Yaohua. Wang. 1,2. . Arash. Tavakkol. 1. Lois Orosa. 1,4. . Saugata. . Ghose. 3. . Nika Mansouri Ghiasi. 1. . Minesh. Patel. Unclonable. Functions . by Exploiting the Latency-Reliability Tradeoff . in Modern Commodity DRAM Devices. Jeremie. S. Kim. . Minesh. Patel . Hasan Hassan . . Onur. . Mutlu. . Motivation.

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