PPT-TLC: A Tag-less Cache for reducing dynamic first level Cache Energy

Author : marina-yarberry | Published Date : 2019-12-11

TLC A Tagless Cache for reducing dynamic first level Cache Energy Presented by Rohit Reddy Takkala Introduction First level caches are performance critical and are

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TLC: A Tag-less Cache for reducing dynamic first level Cache Energy: Transcript

TLC A Tagless Cache for reducing dynamic first level Cache Energy Presented by Rohit Reddy Takkala Introduction First level caches are performance critical and are therefore optimized for speed Modern processors reduce the miss ratio by using setassociative caches and optimize latency by reading all ways in parallel with the TLBTranslation Lookaside Buffer and tag lookup. Exploiting . Spatial Locality . for Energy-Optimized . Compressed Caching. Somayeh. . Sardashti. and . David . A. . Wood. University . of Wisconsin-Madison. 1. 2. 3. Where does energy go?. Communication vs. Computation. Varun. . Mathur. Mingwei. Liu. 1. I-cache and address tag . Instruction cache has. Large chip area. High access frequency=>switching power. Example:. Direct mapped I-cache. 1024 entries (=>1024 one way sets). Characterizing Roles of Front-end Servers in . End. -to-. End. . Performance of Dynamic Content Distribution . 46842197. Li . ZHANG. 78884704 . . Dakuo WANG. 30165502 . . Xuejie. SUN. (last updated . June 2015). TLC Teams. Team 1. Team 2. Attending. Attending. Fellow. Fellow. Nurse practitioner. Nurse practitioner. 2 Residents. (Anesthesia. and EM). 2 Residents. (Internal medicine). CS 3410, Spring . 2012. Computer Science. Cornell University. See P&H 5.1, 5.2 (except writes). Write-. Back. Memory. Instruction. Fetch. Execute. Instruction. Decode. extend. register. file. control. Web developer since 1995. Pluralsight Author. 3. rd. Degree Black Belt, Tae Kwon Do. ASP.NET MVP. boedie@outlook.com. @boedie. weblogs.asp.net/boedie. About Me. ASP.NET Core. Open source and cross platform. Direct-mapped caches. Set-associative caches. Impact of caches on performance. CS 105. Tour of the Black Holes of Computing. Cache Memories. C. ache memories . are small, fast SRAM-based memories managed automatically in hardware. March 28, 2017. Agenda. Review from last lecture. Cache access. Associativity. Replacement. Cache Performance. Cache Abstraction and Metrics. Cache hit rate = (# hits) / (# hits # misses) = (# hits) / (# accesses). Agenda. Review from last lecture. Data flow model. Memory hierarchy. More Caches. The Dataflow Model (of a Computer). Von Neumann model: An instruction is fetched and executed in . control flow order . 2017. Instructor:. . Ruth Anderson. Teaching Assistants:. Dylan Johnson. Kevin Bi. Linxing. Preston Jiang. Cody . Ohlsen. Yufang. Sun. Joshua Curtis. Administrivia. Office Hours Changes – check calendar!!. Instructor:. . Mark Wyse. Teaching Assistants:. Kevin Bi. Parker . DeWilde. Emily . Furst. Sarah House. Waylon Huang. Vinny . Palaniappan. Administrative. Lab 3 due . Friday . (2/16). Homework 4 released today (Structs, Caches). 1 Memory & Cache Memories: Review 2 Memory is required for storing Data Instructions Different memory types Dynamic RAM Static RAM Read-only memory (ROM) Characteristics Access time Price Volatility . Survey Results. Education Committee Meeting. November 2, 2016. TLC Survey Background. TLC Survey. What the . TLC . Survey IS:. . A statistically valid and reliable instrument to assess whether educators have working conditions in their school that support effective teaching. . S. Narravula, P. Balaji, K. Vaidyanathan, . H.-W. Jin and D. K. Panda. The Ohio State University. Presentation Outline. Introduction/Motivation. Design and Implementation. Experimental Results. Conclusions.

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