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LOGISTICS AND INTRODUCTION
CS/ECE 7810: Advanced Computer Architecture
Mahdi Nazm Bojnordi
Assistant Professor
School of Computing
University of Utah
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Advanced Computer Architecture
Basics of Computer Systems: CPU,
Memory, Storage, IO, etc.
Processor/Memory Performance
Optimization: ILP, TLP, AMAT, etc.
Today/Future Concerns:
Power Wall, Energy-efficiency,
Security, etc.
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Course organization and rules
Logistics
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Instructor
¨ Mahdi Nazm Bojnordi
¤ Assistant Professor, School of Computing
¤ PhD degree in Electrical Engineering (2016)
¤ Worked in industry for four years (before PhD)
¨ Research in Computer Architecture
¤ Energy-efficient computing
¤ Emerging memory technologies
¨ Office Hours
¤ Please email me for appointment
¤ MEB 3418
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This Course
¨ Prerequisite¤ CS/ECE 6810: Computer Architecture
¨ Advanced topics in computer architecture¤ cache energy innovations¤ memory system optimizations¤ interconnection networks¤ cache coherence protocols¤ emerging computation models
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Resources
¨ Recommended books and references¤ “Memory Systems: Cache, DRAM, Disk”, Jacob et al¤ “Principles and Practices of Interconnection Networks”,
Dally and Towles¤ “Parallel Computer Architecture”, Culler, Singh, Gupta¤ “Synthesis Lectures on Computer Architecture”, Morgan
& Claypool Publishers
¨ Class webpage¤ http://www.cs.utah.edu/~bojnordi/classes/7810/s21/
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Class Webpage
¨ Please visit online!
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Course Expectation
¨ Use Canvas for all of your submissions¤ No scanned handwritten documents please!
¨ Grading
Fraction Notes
Project 50% One simulation-based project
Homework 20% One homework assignment
Paper presentation 10% One in class paper presentation
Final 20%
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Course Project
¨ A creative, simulation-based project on¤ Memory system optimization (SRAM, DRAM, RRAM, etc.)¤ Data movement optimizations (Off/On–chip interfaces)¤ Hardware accelerators (GPU, FPGA, ASIC)¤ …
¨ Form a group of 2 people by Feb. 2¨ Choose your topic by Feb. 10¨ Prepare for an in-class presentation in April¨ Prepare a conference-style report by end of May
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Paper Presentation and Assignment
¨ Every student presents a paper in class¤ A related work on your course project is recommended¤ Three main components must be included
n The goal and key idean Strengths and weaknessesn Future work
¤ Email me your paper by Mar. 29n Conferences such as ISCA, MICRO, ASPLOS, HPCA
¨ A homework assignment will be posted on Feb. 24¤ Due on Mar. 4 (11:59PM)
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Academic Integrity
¨ Do NOT cheat!!¤ Disciplinary hearings are no fun
¤ Please read the Policy Statement on Academic Misconduct, carefully.
¤ We have no tolerance for cheating
¨ Also, read the College of Engineering Guidelines for disabilities, add, drop, appeals, etc.
¨ For more information, please refer to the important policies on the class webpage.
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About You …
¨ Are you working in a research area?
¨ Do you know programming languages?¤ C/C++, etc.
¨ Do you know any hardware description languages?¤ Verilog
¨ Are you familiar with simulators?
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The importance of energy efficient computing
Energy-efficient Computing
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Energy and Power Trends
¨ Power consumption is increasing significantly
0
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11.5
2
2.5
33.5
4
4.5
2010 2012 2015 2018 2021Proc
esso
r Po
wer
Nor
mal
ized
to
201
0
Year
(data source: ITRS, DarkSilicon’11)
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CPU Power Consumption
¨ Major power consumption issues
Peak Power/Power Density Average Power
q Heato Packaging, cooling,
component spacingq Switching noise
o Decoupling capacitors
q Battery lifeo Bulkier battery
q Utility costso Probability, cannot run
your business!
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New Challenges
¨ Excessive energy consumption¤ More energy-efficient architectures are needed
200M wearable devices will be sold in 2019 (source: IDC forecast)
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New Challenges
¨ Power delivery and cooling systems¤ More energy-efficient architectures are required
Facebook datacenter at edge of the Arctic circle (source: CNET, 2013)
Microsoft underwater datacenter(source: NYTimes, 2016)
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The High Cost of Data Movement
Processor
¨ Data movement is the primary contributor to energy dissipation in nanometer ICs.
RelativeEnergy Costs
Source: NVidia
A
B
DRAM Module
A + B
500x 10x 1x
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Data Movement Energy Increasing
0
0.2
0.4
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0.8
1
90 65 45 32 22 14 10 7
Technology (nm)
Rel
ativ
e En
ergy
Compute Energy
Interconnect Energy
Shekhar Borkar, Journal of Lightwave Technology, 2013
¨ By 2020, the energy cost of moving data across the memory hierarchy will be orders of magnitude higher than the cost of performing a floating-point operation.
-- U.S. Department of Energy, 2014
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Possible Solutions
¨ How to minimize data movement energy?
ProcessorDRAM Module
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Problem: Energy Efficiency
¨ Unconventional solutions are needed!¤ Hardware¤ Software
Solar powered dresses(source: www.ecochunk.com)
Harvesting motion energy(source: www.ecouterre.com)
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Hardware Architecture
“People who are really serious about software should make their own hardware.”
— Alan Kay
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Research Examples
Last Level Cache
Core 1 Core N
Controller
3D Stacked Memory Dice
2. Bandwidth and Energy Efficient Interface
3. Efficient In-Package Memory Systems
4. Non-von Neumann Computing In Memory Modules with Emerging Technologies
¨ Goal: enable energy and bandwidth efficient data movement between memory and the processor cores.
Main Memory
1. Energy efficient data encoding for large on-die cache
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Memory Bandwidth Demand
¨ Currently, unfathomable amounts of data generated in various domains
Large Synoptic Survey Telescope [lsst.org]
Large Hadron Collider [HSF-CWP-2017-01]US Walmart Supermarkets [dezyre.com]
petabytes per hour hundreds of petabytes exabytes
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Memory Bandwidth Demand
¨ Currently, unfathomable amounts of data generated in various domains
¨ By 2030, the required bandwidth for fully autonomous self-driving car is expected to reach near 1TB per second.
petabytes per hour hundreds of petabytes
Large Synoptic Survey Telescope [lsst.org]
exabytes
Large Hadron Collider [HSF-CWP-2017-01]US Walmart Supermarkets [dezyre.com]
-- Micron 2018
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Emerging Technologies
¨ High bandwidth memory
Off-chip Memory 3D Stacked Memory
Lower BandwidthLower Costs
Higher BandwidthHigher Costs
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Emerging Non-volatile Memories
¨ Use resistive states to represent info.¤ Can we build non-von Neumann machines?
n In-Memory and In-situ computers