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Estimation of Single Event Upset Probability Impact of FPGA Designs. Prasanna Sundararajan, Scott McMillan, Brandon Blodget, Carl Carmichael, Xilinx Inc Cameron Patterson Virginia Tech. Introduction.

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estimation of single event upset probability impact of fpga designs

Estimation of Single Event Upset Probability Impact of FPGA Designs

Prasanna Sundararajan, Scott McMillan,

Brandon Blodget, Carl Carmichael,

Xilinx Inc

Cameron Patterson

Virginia Tech

introduction
Introduction
  • Single Event Upsets (SEUs) on a SRAM based FPGA may impact the functionality of the programmed circuit
  • FPGA designs do not utilize all memory cells
  • SEUPI tool estimates the probability that an SEU will alter a memory cell utilized by a specific design
  • SEUPI augments Mean Time Between Failure (MTBF) calculations with design specific information
motivation
Motivation
  • Increase in use of SRAM FPGAs in space applications
  • Provide accurate MTBF information in order to make intelligent mitigation decisions
  • Rise in configuration cells mandates a SEU susceptibility estimation for FPGA systems placed in high-altitude to determine a need for mitigation strategy
single event upset
VDD

VDD

OFF

ON

Sensitive Area

Sensitive Area

I

Q Difff

ON

OFF

GND

t(nS)

Single Event Upset
  • A single high-energy particle can strike a critical node and leave behind an ionized track
  • If value of this charge is high enough, a voltage of sufficient value can cause a bit flip called soft error.
single event upsets
Single Event Upsets
  • Caused by
    • Atmospheric neutrons
    • Alpha particles
  • Soft error can be mitigated by reconfiguring the FPGA configuration memory
  • SEUs in configuration memory of FPGA is the focus of this paper
seu impact dependency factors
SEU Impact Dependency Factors
  • SEU Impact Depends on
    • Altitude: ~10x worse @10,000Ft vs. sea level
    • Latitude: ~6x worse at North Pole vs. Equator
    • Neutron Flux: 120neutrons/cm2-hr impact everything (@ 45° latitude)
    • Area cross section per configuration bit
    • % Resource utilization of a FPGA Device
fpga architectures and configuration bits
0.18 m

0.35 m

0.22 m

0.15 m

FPGA Architectures And Configuration Bits
  • Rise in configuration bits due to advancement in process technology
alternate methods
Alternate Methods
  • Controlled exposure of FPGAs to high energy particle beam
    • Performed by exposing FPGAs to high energy particle generator
  • SEU study at Xilinx
    • Large number of FPGAs exposed to atmosphere and upsets are recorded
alternate methods1
Alternate Methods
  • Hardware SEU simulator by Los Alamos Laboratory and Brigham Young University
    • SEU simulated by dynamically corrupting one bit at a time
  • Mean Time Between Upset
    • SEU susceptibility estimated by assuming all the device configuration bits are susceptible to SEUs
single event upset probability impact seupi
Single Event Upset Probability Impact (SEUPI)
  • Tool developed for static estimation of bits susceptible to SEUs
  • Estimates provided specific to a FPGA design
  • Estimation technique based on accounting the resources used in a user design
  • SEUPI is a ratio of bits used in a specific design to total number of device configuration bits
single event upset probability impact seupi1
Device

Config Bits

%

Care Bits

Single Event Upset Probability Impact (SEUPI)
  • Estimate % of configuration bits used in a design a.k.a Care Bits
  • Care bits depends on resource (pips, muxes, LUTs, FFs) utilization in a design
seupi tool flow
FPGA Design

NCD

Tools to identify resource used

Resource Usage List

SEUPI

Map Resource & Bits

JBits Data Model

SEUPI Tool Flow

Report Care Bits

Device Resource &

Config Bits Model

results
Results
  • Design 1
    • 8109/10752 slices (75%)
    • 369/624 IOBs (59%)
    • 27628 Signals
  • Design 2
    • 19401/22400 slices (86%)
    • 408/912 IOBs (45%)
    • 56121 Signals
seupi pros cons
SEUPI Pros & Cons
  • Pros
    • No investment needed to procure hardware simulator
    • Controlled experiment using high energy particle accelerators can be avoided
    • Inexpensive estimate specific to an user design
    • Suitable if worst case static estimate is desired
  • Cons
    • As this is a worst case estimate, the susceptibility estimate is high compared to other estimation techniques
mtbf calculation
MTBF Calculation
  • Mean Time Between Failure
    • MTBU = 1  (Area cross section per bit * neutron flux * device config bits)
    • SEUPI = Care Bits / Total Device Configuration Bits
    • MTBF = MTBU / SEUPI
summary future work
Summary & Future Work
  • Estimation of susceptibility to SEU important to build reliable FPGA systems
  • SEUPI tool can be used for static estimation
  • SEUPI tool can be used to estimate without the need for SEU hardware or high energy accelerators
  • Results obtained from SEUPI tool would be validated with hardware simulator tool
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