SlideShare ist ein Scribd-Unternehmen logo
1 von 23
Architecture Exploration
of RISC-V Processor and
Comparison with ARM
Cortex A53
Karthikeyan S
Architecture Modeling Intern
Mirabilis Design
karthi.sugumar@gmail.com
Tom Jose
Application Specialist
Mirabilis Design
tjose@mirabilisdesign.com
2
Problem
 Limited devices for understanding RISC-V
 No software platform for observation
Solution
 Ease of understanding using System-Level
Modeling Technique
 VisualSim Architect provides a platform for
observation and analysis
3
 Develop and Test Processor designs using
Processor Generator Package
 Establish and Observe current and upcoming real-
world applications
 Compare and benchmark a variety of hardware and
software implementations
Features of VisualSim
RISC-V Processor Modeling
Let’s start with how we used VisualSim Architect
to model the Processor
5
RISC-V ISA
 Built using VisualSim’s Processor Generator
Technology
 Specifications of SiFive’s E31 Core was referred
 RV32I is the ISA used
Instruction Cycles
ADD 1
MUL 2
DIV Min: 2 Max: 33
LW, SW 2
6
RISC-V Processor Specs
 Processor Bits – 32
 ISA – RV32I
 Clock Speed – 500 MHz
 Pipeline type – In-Order
 Pipeline Stages - 5
 Cache – 32 KBytes of I-Cache and D-Cache
- 64 KBytes of L2 Cache
7
Representation of System-Level RISC-V Processor
VisualSim Schematic
8
Task Profile
 NpBench: Benchmarking Suite for Network
Processors was used to generate the task profile
8
Task
Number of
Instructions
Integer/
Floating
(%)
Load/Store
(%)
Shift
(%)
Logic
(%)
Branch
(%)
Others
(%)
CRC 276 15 30 10 15 15 5
FRAG 573 38 14 4 13 27 4
RED 802 38 24 8 0 21 9
MD5 3147 44 9 13 18 14 4
SSLD 828 51 11 0 0 35 3
WFQ 905 24 19 17 0 34 5
MTC 2427 33 27 3 10 18 9
MPLS-UP 1603 31 14 9 8 34 4
MPLS-DOWN 1574 32 15 9 8 32 4
AES 785 14 23 17 22 20 4
Instruction Mix Table for Network Task Simulation
9
Results – Task Latency
Time taken for completion of every task
10
10Time taken for completion of every task set (10 Tasks)
Results – Task Set Latency
11
Results – Average Power Consumption
Average Power Consumed by the Processor
RISC-V
26.28 uW/MHz
~13mW @ 500
Mhz
Comparison with ARM
Cortex A53
Let’s see how a RISC-V Core compares with A53
13
A53 Specs
 Processor Bits – 64
 ISA – ARM v8
 Clock Speed – 500 MHz
 Pipeline type – In-Order
 Pipeline Stages - 8
 Cache - 64-KBytes of I-Cache and D-Cache
- 512 KBytes of L2 Cache
Modeling technique was the same as RISC-V’s
Same task profile was used for simulation
14
A53 RISC-V
RISC-V has a slightly higher task completion rate
Task Latency
15
RISC-V completes 1 Extra Task Set
Task Set Latency
16
Power Consumtion
16
A53
RISC-V shines when it comes to Power Consumption
RISC-V
Samsung Exynos 5433
38mW @ 500 MHz
RISC-V
26.28uW/MHz 
13mW @ 500 Mhz
17
13
37
0
5
10
15
20
25
30
35
40
Power(mW)
Average Power Consumption
RISC-V Cortex A53
RISC-V consumes 3 TIMES LESSER power than Cortex A53
18
RISC-V based Solid-State Drive !
Another application of RISC-V : Solid-State Drives
19
Read/Write Latencies and Average Power
Results obtained from system-level model of a RISC-V
based Solid-State Drive
Traffic Rate: 30us Traffic Rate: 10us
20
VisualSim Explorer
Try these links to get the feel of VisualSim
Requirements  Just a browser and a Java Runtime Environment
Links
 RISC-V Processor System
http://www.mirabilisdesign.com/launchdemo/demo/HAL/RISC_
V/RISCV_InOrder/
 ARM Cortex A53 Processor System
http://www.mirabilisdesign.com/launchdemo/demo/HAL/A_Cort
ex/ARM_Cortex_A53/
 RISC-V based Solid-State Drive
http://www.mirabilisdesign.com/launchdemo/demo/system_arch
itecture/SSD/SSD_RISC_V/
21
Future Development
 A 64 bit RISC-V System-Level Model
 Machine Learning Applications
 Multi-Core SoC Design
22
Conclusion
 Successfully simulated RISC-V ISA as a Processor
Core
 Compared RISC-V Core with ARM Cortex A53
using a network application
 Showcased a Solid-State Drive Design using the
RISC-V Processor Core
Thank You
Get back to us at www.mirabilisdesign.com
karthi.sugumar@gmail.com | tjose@mirabilisdesign.com

Weitere ähnliche Inhalte

Was ist angesagt?

Compare Performance-power of Arm Cortex vs RISC-V for AI applications_oct_2021
Compare Performance-power of Arm Cortex vs RISC-V for AI applications_oct_2021Compare Performance-power of Arm Cortex vs RISC-V for AI applications_oct_2021
Compare Performance-power of Arm Cortex vs RISC-V for AI applications_oct_2021Deepak Shankar
 
Introduction to arm architecture
Introduction to arm architectureIntroduction to arm architecture
Introduction to arm architectureZakaria Gomaa
 
Arm Processors Architectures
Arm Processors ArchitecturesArm Processors Architectures
Arm Processors ArchitecturesMohammed Hilal
 
Tutorial getting started with RISC-V verification
Tutorial getting started with RISC-V verificationTutorial getting started with RISC-V verification
Tutorial getting started with RISC-V verificationRISC-V International
 
Closing the RISC-V compliance gap via fuzzing
Closing the RISC-V compliance gap via fuzzingClosing the RISC-V compliance gap via fuzzing
Closing the RISC-V compliance gap via fuzzingRISC-V International
 
Slideshare - PCIe
Slideshare - PCIeSlideshare - PCIe
Slideshare - PCIeJin Wu
 
Basics of boot-loader
Basics of boot-loaderBasics of boot-loader
Basics of boot-loaderiamumr
 
Uboot startup sequence
Uboot startup sequenceUboot startup sequence
Uboot startup sequenceHoucheng Lin
 
Reverse Engineering of Rocket Chip
Reverse Engineering of Rocket ChipReverse Engineering of Rocket Chip
Reverse Engineering of Rocket ChipRISC-V International
 
The Future of Operating Systems on RISC-V
The Future of Operating Systems on RISC-VThe Future of Operating Systems on RISC-V
The Future of Operating Systems on RISC-VC4Media
 
Creating Your Own PCI Express System Using FPGAs: Embedded World 2010
Creating Your Own PCI Express System Using FPGAs: Embedded World 2010Creating Your Own PCI Express System Using FPGAs: Embedded World 2010
Creating Your Own PCI Express System Using FPGAs: Embedded World 2010Altera Corporation
 

Was ist angesagt? (20)

Compare Performance-power of Arm Cortex vs RISC-V for AI applications_oct_2021
Compare Performance-power of Arm Cortex vs RISC-V for AI applications_oct_2021Compare Performance-power of Arm Cortex vs RISC-V for AI applications_oct_2021
Compare Performance-power of Arm Cortex vs RISC-V for AI applications_oct_2021
 
SoC: System On Chip
SoC: System On ChipSoC: System On Chip
SoC: System On Chip
 
Introduction to arm architecture
Introduction to arm architectureIntroduction to arm architecture
Introduction to arm architecture
 
Arm Processors Architectures
Arm Processors ArchitecturesArm Processors Architectures
Arm Processors Architectures
 
PCI Drivers
PCI DriversPCI Drivers
PCI Drivers
 
Tutorial getting started with RISC-V verification
Tutorial getting started with RISC-V verificationTutorial getting started with RISC-V verification
Tutorial getting started with RISC-V verification
 
Closing the RISC-V compliance gap via fuzzing
Closing the RISC-V compliance gap via fuzzingClosing the RISC-V compliance gap via fuzzing
Closing the RISC-V compliance gap via fuzzing
 
Slideshare - PCIe
Slideshare - PCIeSlideshare - PCIe
Slideshare - PCIe
 
Embedded Linux on ARM
Embedded Linux on ARMEmbedded Linux on ARM
Embedded Linux on ARM
 
USB Drivers
USB DriversUSB Drivers
USB Drivers
 
Basics of boot-loader
Basics of boot-loaderBasics of boot-loader
Basics of boot-loader
 
What is Bootloader???
What is Bootloader???What is Bootloader???
What is Bootloader???
 
Uboot startup sequence
Uboot startup sequenceUboot startup sequence
Uboot startup sequence
 
Hard ip based SoC design
Hard ip based SoC designHard ip based SoC design
Hard ip based SoC design
 
Linux device drivers
Linux device drivers Linux device drivers
Linux device drivers
 
Reverse Engineering of Rocket Chip
Reverse Engineering of Rocket ChipReverse Engineering of Rocket Chip
Reverse Engineering of Rocket Chip
 
The Future of Operating Systems on RISC-V
The Future of Operating Systems on RISC-VThe Future of Operating Systems on RISC-V
The Future of Operating Systems on RISC-V
 
Pcie drivers basics
Pcie drivers basicsPcie drivers basics
Pcie drivers basics
 
Introduction to ARM
Introduction to ARMIntroduction to ARM
Introduction to ARM
 
Creating Your Own PCI Express System Using FPGAs: Embedded World 2010
Creating Your Own PCI Express System Using FPGAs: Embedded World 2010Creating Your Own PCI Express System Using FPGAs: Embedded World 2010
Creating Your Own PCI Express System Using FPGAs: Embedded World 2010
 

Ähnlich wie Architecture Exploration of RISC-V Processor and Comparison with ARM Cortex-A53

Energy efficient AI workload partitioning on multi-core systems
Energy efficient AI workload partitioning on multi-core systemsEnergy efficient AI workload partitioning on multi-core systems
Energy efficient AI workload partitioning on multi-core systemsDeepak Shankar
 
Balance, Flexibility, and Partnership: An ARM Approach to Future HPC Node Arc...
Balance, Flexibility, and Partnership: An ARM Approach to Future HPC Node Arc...Balance, Flexibility, and Partnership: An ARM Approach to Future HPC Node Arc...
Balance, Flexibility, and Partnership: An ARM Approach to Future HPC Node Arc...Eric Van Hensbergen
 
Literature Summary.pptx
Literature Summary.pptxLiterature Summary.pptx
Literature Summary.pptxHazoor Ahmad
 
Computer Organization.pptx
Computer Organization.pptxComputer Organization.pptx
Computer Organization.pptxsaimagul310
 
RISC-V & SoC Architectural Exploration for AI and ML Accelerators
RISC-V & SoC Architectural Exploration for AI and ML AcceleratorsRISC-V & SoC Architectural Exploration for AI and ML Accelerators
RISC-V & SoC Architectural Exploration for AI and ML AcceleratorsRISC-V International
 
SNAPDRAGON SoC Family and ARM Architecture
SNAPDRAGON SoC Family and ARM Architecture SNAPDRAGON SoC Family and ARM Architecture
SNAPDRAGON SoC Family and ARM Architecture Abdullaziz Tagawy
 
Risc and cisc eugene clewlow
Risc and cisc   eugene clewlowRisc and cisc   eugene clewlow
Risc and cisc eugene clewlowManish Prajapati
 
Risc and cisc eugene clewlow
Risc and cisc   eugene clewlowRisc and cisc   eugene clewlow
Risc and cisc eugene clewlowChaudhary Manzoor
 
Risc and cisc eugene clewlow
Risc and cisc   eugene clewlowRisc and cisc   eugene clewlow
Risc and cisc eugene clewlowkaran saini
 
ARM AAE - Developing Code for ARM
ARM AAE - Developing Code for ARMARM AAE - Developing Code for ARM
ARM AAE - Developing Code for ARMAnh Dung NGUYEN
 
RISC and ARM contollers Design-Philosophy.pptx
RISC and ARM contollers Design-Philosophy.pptxRISC and ARM contollers Design-Philosophy.pptx
RISC and ARM contollers Design-Philosophy.pptxcontactamitsuryavans
 
CS304PC:Computer Organization and Architecture UNIT V_merged_merged.pdf
CS304PC:Computer Organization and Architecture UNIT V_merged_merged.pdfCS304PC:Computer Organization and Architecture UNIT V_merged_merged.pdf
CS304PC:Computer Organization and Architecture UNIT V_merged_merged.pdfAsst.prof M.Gokilavani
 
Microcontroller(18CS44) module 1
Microcontroller(18CS44)  module 1Microcontroller(18CS44)  module 1
Microcontroller(18CS44) module 1Swetha A
 
Necessity of 32-Bit Controllers
Necessity of 32-Bit ControllersNecessity of 32-Bit Controllers
Necessity of 32-Bit Controllersmohanav
 

Ähnlich wie Architecture Exploration of RISC-V Processor and Comparison with ARM Cortex-A53 (20)

Energy efficient AI workload partitioning on multi-core systems
Energy efficient AI workload partitioning on multi-core systemsEnergy efficient AI workload partitioning on multi-core systems
Energy efficient AI workload partitioning on multi-core systems
 
Balance, Flexibility, and Partnership: An ARM Approach to Future HPC Node Arc...
Balance, Flexibility, and Partnership: An ARM Approach to Future HPC Node Arc...Balance, Flexibility, and Partnership: An ARM Approach to Future HPC Node Arc...
Balance, Flexibility, and Partnership: An ARM Approach to Future HPC Node Arc...
 
Webinar on RISC-V
Webinar on RISC-VWebinar on RISC-V
Webinar on RISC-V
 
CISC & RISC Architecture
CISC & RISC Architecture CISC & RISC Architecture
CISC & RISC Architecture
 
10_Pipeline.pdf
10_Pipeline.pdf10_Pipeline.pdf
10_Pipeline.pdf
 
Literature Summary.pptx
Literature Summary.pptxLiterature Summary.pptx
Literature Summary.pptx
 
Computer Organization.pptx
Computer Organization.pptxComputer Organization.pptx
Computer Organization.pptx
 
Hg3612911294
Hg3612911294Hg3612911294
Hg3612911294
 
RISC-V & SoC Architectural Exploration for AI and ML Accelerators
RISC-V & SoC Architectural Exploration for AI and ML AcceleratorsRISC-V & SoC Architectural Exploration for AI and ML Accelerators
RISC-V & SoC Architectural Exploration for AI and ML Accelerators
 
SNAPDRAGON SoC Family and ARM Architecture
SNAPDRAGON SoC Family and ARM Architecture SNAPDRAGON SoC Family and ARM Architecture
SNAPDRAGON SoC Family and ARM Architecture
 
Andes RISC-V processor solutions
Andes RISC-V processor solutionsAndes RISC-V processor solutions
Andes RISC-V processor solutions
 
Risc and cisc eugene clewlow
Risc and cisc   eugene clewlowRisc and cisc   eugene clewlow
Risc and cisc eugene clewlow
 
Risc and cisc eugene clewlow
Risc and cisc   eugene clewlowRisc and cisc   eugene clewlow
Risc and cisc eugene clewlow
 
Risc and cisc eugene clewlow
Risc and cisc   eugene clewlowRisc and cisc   eugene clewlow
Risc and cisc eugene clewlow
 
ARM AAE - Developing Code for ARM
ARM AAE - Developing Code for ARMARM AAE - Developing Code for ARM
ARM AAE - Developing Code for ARM
 
RISC and ARM contollers Design-Philosophy.pptx
RISC and ARM contollers Design-Philosophy.pptxRISC and ARM contollers Design-Philosophy.pptx
RISC and ARM contollers Design-Philosophy.pptx
 
Processors selection
Processors selectionProcessors selection
Processors selection
 
CS304PC:Computer Organization and Architecture UNIT V_merged_merged.pdf
CS304PC:Computer Organization and Architecture UNIT V_merged_merged.pdfCS304PC:Computer Organization and Architecture UNIT V_merged_merged.pdf
CS304PC:Computer Organization and Architecture UNIT V_merged_merged.pdf
 
Microcontroller(18CS44) module 1
Microcontroller(18CS44)  module 1Microcontroller(18CS44)  module 1
Microcontroller(18CS44) module 1
 
Necessity of 32-Bit Controllers
Necessity of 32-Bit ControllersNecessity of 32-Bit Controllers
Necessity of 32-Bit Controllers
 

Kürzlich hochgeladen

Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Zilliz
 
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyArtificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyKhushali Kathiriya
 
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In PakistanCNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistandanishmna97
 
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsMS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsNanddeep Nachan
 
FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024The Digital Insurer
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDropbox
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherRemote DBA Services
 
Elevate Developer Efficiency & build GenAI Application with Amazon Q​
Elevate Developer Efficiency & build GenAI Application with Amazon Q​Elevate Developer Efficiency & build GenAI Application with Amazon Q​
Elevate Developer Efficiency & build GenAI Application with Amazon Q​Bhuvaneswari Subramani
 
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingRepurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingEdi Saputra
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...DianaGray10
 
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...Angeliki Cooney
 
Vector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxVector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxRemote DBA Services
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMESafe Software
 
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdfSandro Moreira
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businesspanagenda
 
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MIND CTI
 
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...apidays
 
ICT role in 21st century education and its challenges
ICT role in 21st century education and its challengesICT role in 21st century education and its challenges
ICT role in 21st century education and its challengesrafiqahmad00786416
 
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...apidays
 

Kürzlich hochgeladen (20)

Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)
 
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyArtificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : Uncertainty
 
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In PakistanCNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistan
 
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsMS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectors
 
FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor Presentation
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
 
Elevate Developer Efficiency & build GenAI Application with Amazon Q​
Elevate Developer Efficiency & build GenAI Application with Amazon Q​Elevate Developer Efficiency & build GenAI Application with Amazon Q​
Elevate Developer Efficiency & build GenAI Application with Amazon Q​
 
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingRepurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
 
Understanding the FAA Part 107 License ..
Understanding the FAA Part 107 License ..Understanding the FAA Part 107 License ..
Understanding the FAA Part 107 License ..
 
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
 
Vector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxVector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptx
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
 
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024
 
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
 
ICT role in 21st century education and its challenges
ICT role in 21st century education and its challengesICT role in 21st century education and its challenges
ICT role in 21st century education and its challenges
 
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
 

Architecture Exploration of RISC-V Processor and Comparison with ARM Cortex-A53

  • 1. Architecture Exploration of RISC-V Processor and Comparison with ARM Cortex A53 Karthikeyan S Architecture Modeling Intern Mirabilis Design karthi.sugumar@gmail.com Tom Jose Application Specialist Mirabilis Design tjose@mirabilisdesign.com
  • 2. 2 Problem  Limited devices for understanding RISC-V  No software platform for observation Solution  Ease of understanding using System-Level Modeling Technique  VisualSim Architect provides a platform for observation and analysis
  • 3. 3  Develop and Test Processor designs using Processor Generator Package  Establish and Observe current and upcoming real- world applications  Compare and benchmark a variety of hardware and software implementations Features of VisualSim
  • 4. RISC-V Processor Modeling Let’s start with how we used VisualSim Architect to model the Processor
  • 5. 5 RISC-V ISA  Built using VisualSim’s Processor Generator Technology  Specifications of SiFive’s E31 Core was referred  RV32I is the ISA used Instruction Cycles ADD 1 MUL 2 DIV Min: 2 Max: 33 LW, SW 2
  • 6. 6 RISC-V Processor Specs  Processor Bits – 32  ISA – RV32I  Clock Speed – 500 MHz  Pipeline type – In-Order  Pipeline Stages - 5  Cache – 32 KBytes of I-Cache and D-Cache - 64 KBytes of L2 Cache
  • 7. 7 Representation of System-Level RISC-V Processor VisualSim Schematic
  • 8. 8 Task Profile  NpBench: Benchmarking Suite for Network Processors was used to generate the task profile 8 Task Number of Instructions Integer/ Floating (%) Load/Store (%) Shift (%) Logic (%) Branch (%) Others (%) CRC 276 15 30 10 15 15 5 FRAG 573 38 14 4 13 27 4 RED 802 38 24 8 0 21 9 MD5 3147 44 9 13 18 14 4 SSLD 828 51 11 0 0 35 3 WFQ 905 24 19 17 0 34 5 MTC 2427 33 27 3 10 18 9 MPLS-UP 1603 31 14 9 8 34 4 MPLS-DOWN 1574 32 15 9 8 32 4 AES 785 14 23 17 22 20 4 Instruction Mix Table for Network Task Simulation
  • 9. 9 Results – Task Latency Time taken for completion of every task
  • 10. 10 10Time taken for completion of every task set (10 Tasks) Results – Task Set Latency
  • 11. 11 Results – Average Power Consumption Average Power Consumed by the Processor RISC-V 26.28 uW/MHz ~13mW @ 500 Mhz
  • 12. Comparison with ARM Cortex A53 Let’s see how a RISC-V Core compares with A53
  • 13. 13 A53 Specs  Processor Bits – 64  ISA – ARM v8  Clock Speed – 500 MHz  Pipeline type – In-Order  Pipeline Stages - 8  Cache - 64-KBytes of I-Cache and D-Cache - 512 KBytes of L2 Cache Modeling technique was the same as RISC-V’s Same task profile was used for simulation
  • 14. 14 A53 RISC-V RISC-V has a slightly higher task completion rate Task Latency
  • 15. 15 RISC-V completes 1 Extra Task Set Task Set Latency
  • 16. 16 Power Consumtion 16 A53 RISC-V shines when it comes to Power Consumption RISC-V Samsung Exynos 5433 38mW @ 500 MHz RISC-V 26.28uW/MHz  13mW @ 500 Mhz
  • 17. 17 13 37 0 5 10 15 20 25 30 35 40 Power(mW) Average Power Consumption RISC-V Cortex A53 RISC-V consumes 3 TIMES LESSER power than Cortex A53
  • 18. 18 RISC-V based Solid-State Drive ! Another application of RISC-V : Solid-State Drives
  • 19. 19 Read/Write Latencies and Average Power Results obtained from system-level model of a RISC-V based Solid-State Drive Traffic Rate: 30us Traffic Rate: 10us
  • 20. 20 VisualSim Explorer Try these links to get the feel of VisualSim Requirements  Just a browser and a Java Runtime Environment Links  RISC-V Processor System http://www.mirabilisdesign.com/launchdemo/demo/HAL/RISC_ V/RISCV_InOrder/  ARM Cortex A53 Processor System http://www.mirabilisdesign.com/launchdemo/demo/HAL/A_Cort ex/ARM_Cortex_A53/  RISC-V based Solid-State Drive http://www.mirabilisdesign.com/launchdemo/demo/system_arch itecture/SSD/SSD_RISC_V/
  • 21. 21 Future Development  A 64 bit RISC-V System-Level Model  Machine Learning Applications  Multi-Core SoC Design
  • 22. 22 Conclusion  Successfully simulated RISC-V ISA as a Processor Core  Compared RISC-V Core with ARM Cortex A53 using a network application  Showcased a Solid-State Drive Design using the RISC-V Processor Core
  • 23. Thank You Get back to us at www.mirabilisdesign.com karthi.sugumar@gmail.com | tjose@mirabilisdesign.com

Hinweis der Redaktion

  1. The problem we are facing today with respect to any new architectural innovation, for instance RISC-V, is the presence of little to no devices actually using it. We are starting to see companies like SiFive develop RISC-V Cores. But RISC-V still has a long way to go. The main problem that we encounter right now is the availability of limited devices to understand RISC-V. Another problem we face is the absence of a reliable platform for observing new architectures. By this, I mean that there is no simulation tool which has capability of showing how the device works with RISC-V. At Mirabilis Design, we have built a user-friendly simulation environment for easy understanding of current and upcoming technology by using system-level modeling. System-Level Modeling is a software solution to approximately estimate the working of real-time hardware implementations through computer simulations. VisualSim Architect is a simulation platform based on system-level modeling and is a product of Mirabilis Design. The software allows users to design, observe, and analyse models built based on real-world implementations and proposals.
  2. VisualSim Architect consists of several design packages of which the Processor Generator Package was used for this project. It provides the user with blocks that can define the ISA, the Processor, the caches used with the processor and the architecture setup which is used to get the statistics of the processor. It can be used to establish and observe current and upcoming real-world applications. It also plays an important role in comparing and benchmarking various hardware and software implementations.
  3. Now that you have a small idea of what VisualSim is, let’s start with how we modeled RISC-V using it.
  4. We’ll start with how the RISC-V ISA was modelled in VisualSim. As discussed before the Processor Generator package of VisualSim was used to model the basic RISC-V Core for this model. The documentation of SiFive’s E31 Core was referred to get a fundamental idea of the number of clock cycles consumed for a variety of instructions. For this system-level implementation, we are using the 32-bit ISA of RISC-V. The table shows some of the basic instructions used and the number of clock cycles they utilize for execution.
  5. Continuing with the specifications of the processor, we have: [1] The Processor bits set at 32  32-bit processor. [2] The ISA that has been used is the RV32I. [3] The Clock Rate is set at 500 MHz [4] An In-Order execution pipeline [5] The RISC-V Core has a 5-stage pipeline (Fetch, Decode, Execute, Memory Access, Write Back) [6] The Instruction and Data Cache sizes have been set to 32 KBytes and the L2 Cache has been set to 64KBytes
  6. This diagram shows what a model looks like in VisualSim. More particularly this diagram shows how a Processor System looks like in VisualSim. The Task Generator block contains a traffic source which triggers data structures to be sent to the processor. The data structures will contain the instruction set that the processor has to execute. The processor is also connected to a bus, which in turn contains the L2 Cache and the DRAM. This system forms a memory hierarchy that the processor uses when it encounters a miss in its internal Cache. The DMA block is used to give the processor the power to access the DRAM when required. This is another architecture specification that may or may not be used depending on the designer. The Power Table block is used to specify the power consumed by the processor at different stages such as Active, Sleep, Standby, and Wait. The Plots block is a hierarchical block that contains plots such as Task Latency, Task Set Latency, MIPS, Cycles per instruction, etc. The Architecture block at the top links several blocks together and gets the statistics of each component. To get a feel of the online version of VisualSim Architect and to take a look at this model, go to this link. http://www.mirabilisdesign.com/launchdemo/demo/HAL/RISC_V/RISCV_InOrder/
  7. We have seen how the processor model was built using VisualSim. For simulating the model we require a predefined task set. For this purpose the task profile provided by NpBench(A Network Processor Benchmarking Tool) was used. The tabulation shows a list of 10 Tasks each having a different number of instructions and variable percentage of the instruction type. RED (Random Early Detection), SSLD (SSL Dispatcher), MPLS (Multi Protocol Label Switching), AES (Advanced Encryption Standard), MD5 (Message Digestion), FRAG (Packet Fragmentation), CRC (Cyclic Redundancy Check), MTC (Media TransCoding), WFQ (Weighted Fair Queuing). This specific task profile was fed to the processor for simulation and analysis.
  8. After modeling, we can see the results of the plots we have included within the model. This picture shows the Task Latency of the Processor. Each data point shows how long the processor has taken to complete 1 task. Since we are sending the tasks in order, we are able to see a pattern of data points each corresponding to a particular task from the NpBench Table. We can see a range of latencies from 1us to 23us in this plot. http://www.mirabilisdesign.com/launchdemo/demo/HAL/RISC_V/RISCV_InOrder/
  9. This plot shows the Task Set Latencies of the Processor. Since we are sending 10 Tasks in order, it could be helpful at times to know how long it takes to complete the entire set once. On an average, the task set latencies come to about 130 us. http://www.mirabilisdesign.com/launchdemo/demo/HAL/RISC_V/RISCV_InOrder/
  10. Another important result that we were able to see from this model was the power consumption. This plot shows the average power consumed by the processor for the simulation time of 1ms. This average is based on the parameters entered within the Power Table of the model. It has states like Active - 26.28 uW/MHz, Wait - 95% of Active, Idle – 25% of Active, Standby – 10% of Active, and Sleep – 2 % of Active. The Power Table finds out the state at which the processor is currently in and plots the average power consumption. For this model we have chosen the power consumption metric from a research paper that presents a RISC-V core with DSP extensions for scalable IoT Devices. The power parameters entirely depends on the user and this is the reference we have used in this presentation. http://www.mirabilisdesign.com/launchdemo/demo/HAL/RISC_V/RISCV_InOrder/
  11. So now that you have heard about the implementation of RISC-V, lets make a system level comparison between RISC-V and an ARM processor or more specifically, Cortex A53.
  12. Starting with the A53 specifications that we used to model: [1] we used a 64 bit variant of the Cortex A53 [2] the instruction set architecture that we have used is ARM v8-A . The Armv8-A is the latest generation Arm architecture. [3]The clock speed that we are using in our model is 500 MHz [4]The ARM Cortex A53 is an In-Order pipeline processor [5]There are 8 pipeline stages in A53 [6]In the model that we have designed, we have an instruction cache and Data Cache of size 64KBytes along with an L2 cache of size 512KBytes
  13. Similar to the plots we saw for RISC-V. Each dot here denotes a single task. So here we have compared the latency of completing each task in A53 and that of RISC-V. We are able to see a slight variation in the latencies. A53 seems to take a little more time in completing the tasks. To see the results yourself through online version of VisualSim, go to this link: http://www.mirabilisdesign.com/launchdemo/demo/HAL/A_Cortex/ARM_Cortex_A53/
  14. In the previous slide we saw the latency of individual tasks. But what you see here is the latency of a set of 10 tasks. This plot shows the task set latencies of both the RISC-V Model and the ARM Model. We can see that, RISC-V was able to complete 7 Task sets while ARM A53 was only able to complete 6 such task sets. More over the difference in latency between A53 and RISC-V is around 15-20 micro seconds. http://www.mirabilisdesign.com/launchdemo/demo/HAL/A_Cortex/ARM_Cortex_A53/
  15. What we see here is the average power consumption plots of A53 and RISC-V. We have referred Samsung Exynos 5433 for getting details regarding power consumption in A53 and for RISC-V we have referred a research paper. As you can see from the plots, the average power consumption of ARM A53 is found to be around 36.8 milliWatts while the average power consumed by RISC-V is around 12.75 milliWatts. So by using VisualSim, we were able to make a realistic system level comparison between two different processors running on different ISAs but both having the same tasks defined by NpBench. We are also able to see a significant difference in the average power consumption. http://www.mirabilisdesign.com/launchdemo/demo/HAL/A_Cortex/ARM_Cortex_A53/
  16. This slide shows a column graph that illustrates the difference between the power consumption of both the models. We could see here that RISC-V consumes approx. 3 times lesser power than A53. We have arrived at this conclusion by using a specific task profile created by NpBench and also by using a specific configuration for the processor models. This result changes as we change the parameters of the model, but comparisons like this can give an estimation of how the models can work after hardware implementation.
  17. Another device, which we felt, where RISC-V processors could be used are the Solid-State Drives. We have built a simple SSD model with 1 flash module using the processor system discussed before. Instead of executing network tasks, the processor here executes tasks related to Wear Levelling, Encryption & Decryption, Error Correction, and Address Translation. A Gen 3 PCI Express bus acts as an interface between the host and the flash. The NVMe controller stores the incoming requests in queue and pops out requests after each one’s completion. Use this link to see how the SSD model runs in the online version of VisualSim Architect: http://www.mirabilisdesign.com/launchdemo/demo/system_architecture/SSD/SSD_RISC_V/
  18. These plots here show the Read/Write Latencies of the SSD. The first Image was captured when the traffic rate was set at 30 micro seconds while the second one was captured when the traffic rate was 10 micro seconds. The second plot shows the SSD undergoes buffering at some stage of the model. VisualSim gives us a chance to change the parameters and configuration to get the right latencies when the traffic rate is increased. Potential hardware faults and bottleneck, such as this, can be prevented using system-level modeling and simulation even before the design stage. http://www.mirabilisdesign.com/launchdemo/demo/system_architecture/SSD/SSD_RISC_V/
  19. For this presentation, we have used RISC-V as a processor model for a network application as well as an SSD. The benefit of using VisualSim is that we could see results like the amount of power consumed , task latencies, read/write latencies, and also play with the configuration and parameters to get a good simulation even before implementing the model on hardware. These simulations try to provide close approximations of what can be expected with the hardware implementation. From comparative studies like with RISC-V and ARM model, we can find the ratios of performance difference and power consumption so that we can expect a similar ratio during hardware implementation.