cse 291 winter 2009 the fpga ecosystem - ucsd mesl...

CSE 291 Winter 2009

The FPGA Ecosystem

Rajesh GuptaUniversity of California, San Diego

2

Moore’s Law

40048008

80808085

8086286

386486 Pentium ® proc

P6

1

10

100

1970 1980 1990 2000 2010Year

Die

siz

e (m

m)

~7% growth per year~2X growth in 10 years

Die size grows by 14% to satisfy Moore’s LawDie size grows by 14% to satisfy Moore’s Law

Courtesy, Intel

40048008

80808085 8086

286386

486Pentium® proc

P6

0.001

0.01

0.1

1

10

100

1000

1970 1980 1990 2000 2010Year

Tran

sist

ors

(MT)

2X growth in 1.96 years!

Transistors on lead microprocessors double every 2 yearsTransistors on lead microprocessors double every 2 years

Courtesy, Intel

Lead microprocessors frequency doubles every 2 yearsLead microprocessors frequency doubles every 2 years

P6Pentium ® proc

48638628680868085

8080800840040.1

1

10

100

1000

10000

1970 1980 1990 2000 2010

Freq

uenc

y (M

hz)

2X every 2 years

Courtesy, Intel

P6Pentium ® proc

486386

2868086

80858080

80084004

0.1

1

10

100

1971 1974 1978 1985 1992 2000Year

Pow

er (W

atts

)

3

The ITRS: Tao of Scalinghttp://public.itrs.net

Source: Ken Yang, UCLA

4 20074 0.065 micron

4 6.7 GHz on chip clock4 9 wiring levels4 600-3000 pins4 Vdd=0.7-1.1V

4 3.5W / 104W / 190W4 DRAM:

4.29 Gb/chip, 183 mm^2, 2.35 Gb/cm^24 MPU

386 Mtrans/chip, 140 mm^2, 276.1 Mtrans/cm^2

4

Design Abstraction Levels

SYSTEM

GATE

CIRCUIT

VoutVin

CIRCUIT

VoutVin

MODULE

+

DEVICE

n+S D

n+

G

Adapted from Irwin & Nayaranan’s Slides from PSU. Copyright 2002 J. Rabaey et al."

5

Design Process

• Conceptualization: function & structure– HLM, behavioral modeling

• Architecture: structure and organization– microarchitectural implementation

• Logical implementation: gates, modules– logic synthesis, logic verification, static timing analysis

• Circuit implementation: transistors– circuit simulations

• Physical design, verification– floorplanning, placement, routing, dynamic timing analysis

6

Speed Power Cost

High LowVolume

Many Implementation Choices

• Microprocessors• Domain-specific processors

– DSP– Network processors– Microcontrollers

• ASIPs• Reconfigurable SoC• FPGA• Gate-array• ASIC

7

E.g. Degree of Customization of Processor Architecture

• The architecture of the computation engine used to implement desired functionality

• Processor does not have to be programmable– “Processor” not equal to general-purpose processor

Application-specific

Registers

CustomALU

DatapathController

Program memory

Assembly code for:

total = 0for i =1 to …

Control logic and State register

Datamemory

IR PC

Single-purpose (“hardware”)

DatapathController

Controllogic

State register

Datamemory

index

total

+

IR PC

Registerfile

GeneralALU

DatapathController

Program memory

Assembly code for:


Control logic and

State register

Datamemory

General-purpose (“software”)

[Adapted from Embedded Systems Design: A Unified Hardware/Software Introduction. Copyright 2000 Vahid & Givargis]

total = 0for i = 1 to N loop

total += M[i]end loop

8

General-purpose Microprocessors• Programmable device used in a variety of

applications– Also known as “microprocessor”

• Features– Program memory– General datapath with large register file and

general ALU• User benefits

– Low time-to-market and NRE costs– High flexibility

• “Pentium” the most well-known, but there are hundreds of others

IR PC

Registerfile

GeneralALU

DatapathController

Program memory

Assembly code for:


Control logic and

State register

Datamemory


9

Application-specific Instruction Processors, ASIP

• Programmable processor optimized for a particular class of applications having common characteristics– Compromise between general-purpose and

single-purpose processors• Features

– Program memory– Optimized datapath– Special functional units

• Benefits– Some flexibility, good performance, size and

power

IR PC

Registers

CustomALU

DatapathController

Program memory

Assembly code for:


Control logic and

State register

Datamemory


10

Single-purpose ‘Processors,’ or ASIC

• Digital circuit designed to execute exactly one program– a.k.a. coprocessor, accelerator or peripheral

• Features– Contains only the components needed to execute a

single program– No program memory

• Benefits– Fast– Low power– Small size

DatapathController

Control logic

State register

Datamemory

index

total

+


11

E.g. ASIC

• A direct sequence spread spectrum (DSSS) radio receiver ASIC (UCLA)

ASIC FeaturesArea: 4.6 mm x 5.1 mmSpeed: 20 MHz @ 10 McpsTechnology: HP 0.5 μmPower: 16 mW - 120 mW (mode dependent)

@ 20 MHz, 3.3 VAvg. Acquisition Time: 10 μs to 300 μs

12

The Implementation Choice is Important

13

The Co-design Ladder

• In the past:– Hardware and software

design technologies were very different

– Recent maturation of synthesis enables a unified view of hardware and software

• Hardware/software “codesign”

Implementation

Assembly instructions

Machine instructions

Register transfers

Compilers(1960's,1970's)

Assemblers, linkers(1950's, 1960's)

Behavioral synthesis(1990's)

RT synthesis(1980's, 1990's)

Logic synthesis(1970's, 1980's)

Microprocessor plus program bits: “software”

VLSI, ASIC, or PLD implementation: “hardware”

Logic gates

Logic equations / FSM's

Sequential program code (e.g., C, VHDL)

The choice of hardware versus software for a particular function is simply a tradeoff among various design metrics, like performance, power, size, NRE cost, and especially flexibility; there is no

fundamental difference between what hardware or software can implement.[Adapted from Embedded Systems Design: A Unified Hardware/Software Introduction. Copyright 2000 Vahid & Givargis]

14

Map from Behavior to Architecture

[Vincentelli]

15

Four Phases in Creating a Chip

16

Implementation Choices

Custom

Standard CellsCompiled Cells Macro Cells

Cell-based

Pre-diffused(Gate Arrays)

Pre-wired(FPGA's)

Array-based

Semicustom

Digital Circuit Implementation Approaches

Adapted from Digital Integrated Circuits (2nd Edition). Copyright 2002 J. Rabaey et al."

17

Transition to Automation and Regular Structures

Intel 4004 (Intel 4004 (‘‘71)71)Intel 8080Intel 8080 Intel 8085Intel 8085

Intel 8286Intel 8286 Intel 8486Intel 8486Courtesy IntelAdapted from Digital Integrated Circuits (2nd Edition). Copyright 2002 J. Rabaey et al."

18

Cell-based Design (or standard cells)

Routing channel requirements arereduced by presenceof more interconnectlayers

Functionalmodule(RAM,multiplier, …)

Routingchannel

Logic cellFeedthrough cellR

ows

of c

ells


19

Standard Cell - Example

3-input NAND cell(from ST Microelectronics):C = Load capacitanceT = input rise/fall time


20

Automatic Cell Generation

Courtesy Acadabra

Initial transistorgeometries

Placedtransistors

Routedcell

Compactedcell

Finishedcell


21

MacroModules

256×32 (or 8192 bit) SRAMGenerated by hard-macro module generator


22

“Soft” MacroModules

Synopsys DesignCompilerAdapted from Digital Integrated Circuits (2nd Edition). Copyright 2002 J. Rabaey et al."

23

“Intellectual Property”

A Protocol Processor for Wireless


24

Semicustom Design Flow

HDLHDL

Logic SynthesisLogic Synthesis

FloorplanningFloorplanning

PlacementPlacement

RoutingRouting

Tape-out

Circuit ExtractionCircuit Extraction

Pre-Layout Simulation

Pre-Layout Simulation

Post-Layout Simulation

Post-Layout Simulation

StructuralStructural

PhysicalPhysical

BehavioralBehavioralDesign Capture

Des

ign

Itera

tion

Des

ign

Itera

tion


25


Pre-wired(FPGA's)

Array-based

Late-Binding Implementation

Custom

Standard CellsCompiled Cells Macro Cells

Cell-based


Pre-wired(FPGA's)

Array-based

Semicustom

Digital Circuit Implementation Approaches


26

Gate Array — Sea-of-gates

rows of

cells

routing channel

uncommitted

VD D

GND

polysilicon

metal

possiblecontact

In1 In2 In3 In4

Out

UncommitedCell

CommittedCell(4-input NOR)


27

Sea-of-gate Primitive Cells

NMOS

PMOS

Oxide-isolation

PMOS

NMOS

NMOS

Using oxide-isolation Using gate-isolation


28

Prewired Arrays

Classification of prewired arrays (or field-programmable devices):

• Based on Programming Technique– Fuse-based (program-once)– Non-volatile EPROM based– RAM based

• Programmable Logic Style– Array-Based– Look-up Table

• Programmable Interconnect Style– Channel-routing– Mesh networks


29

Antifuse

• Normally high resistance (> 100 MΩ)– on application of

appropriate voltage, the antifuse is changed permanently to a low resistance structure (200-500Ω)

30

Array-Based Programmable Logic

PLA PROM PAL

I5 I4

O0

I3 I2 I1 I0

O1O2O3

Programmable AND array

ProgrammableOR array I5 I4

O0

I3 I2 I1 I0

O1O2O3

Programmable AND array

Fixed OR array

Indicates programmable connection

Indicates fixed connection

O0

I3 I2 I1 I0

O1O2O3

Fixed AND array

ProgrammableOR array


31

Programming a PROM

f0

1 X 2 X 1 X 0

f1NANA: programmed node


32

2-input muxas programmable logic block

FA 0

B

S

1

ConfigurationA B S F=0 0 0 00 X 1 X0 Y 1 Y0 Y X XYX 0 YY 0 XY 1 X X 1 Y1 0 X1 0 Y1 1 1 1

XYXY

XY


33

Logic Cell of Actel Fuse-Based FPGA

A

B

SA Y

1

C

D

SB

1

S0S1

1


34

Look-up Table Based Logic Cell

Out

ln1 ln2

Mem

ory In Out

00 00

01 1

10 1

11 0


35

LUT-Based Logic Cell

Courtesy Xilinx

D4

C1....C4

xxxxxx

D3

D2

D1

F4

F3

F2

F1

Logicfunction

ofxxx

Logicfunction

ofxxx

Logicfunction

ofxxx

xx

xx

4

xxxxxx

xxxxxxxx

xxx

xxxx xxxx xxxx

HP

Bitscontrol

Bitscontrol

Multiplexer Controlledby Configuration Program

x

xx

x

xx

xxx xx

xxxx

x

xxxxxx

xx

x

xx

xxx

xx

Xilinx 4000 Series


36

Array-Based Programmable Wiring

Input/output pinProgrammed interconnection

InterconnectPoint

Horizontaltracks

Vertical tracks

Cell

M


37

Mesh-based Interconnect NetworkSwitch Box

Connect Box

InterconnectPoint

Courtesy Dehon and WawrzyniekAdapted from Digital Integrated Circuits (2nd Edition). Copyright 2002 J. Rabaey et al."

38

Transistor Implementation of Mesh


39

Hierarchical Mesh Network

Use overlayed meshto support longer connections

Reduced fanout and reduced resistance


40

EPLD Block Diagram

MacrocellPrimary inputs

Courtesy AlteraAdapted from Digital Integrated Circuits (2nd Edition). Copyright 2002 J. Rabaey et al."

41

Altera MAX

From Smith97Adapted from Digital Integrated Circuits (2nd Edition). Copyright 2002 J. Rabaey et al."

42

Altera MAX Interconnect Architecture

LAB2

PIA

LAB1

LAB6

tPIA

tPIA

row channelcolumn channel

LAB

Courtesy AlteraAdapted from Digital Integrated Circuits (2nd Edition). Copyright 2002 J. Rabaey et al."

Array-based(MAX 3000-7000)

Mesh-based(MAX 9000)

43

Field-Programmable Gate ArraysFuse-based

I/O Buffers

Program/Test/Diagnostics

I/O Buffers

I/O B

uffe

rs

I/O B

uffe

rs

Vertical routes

Rows of logic modulesRouting channels

Standard-cell likefloorplan


44

Xilinx 4000 Interconnect Architecture

2

12

8

4

3

2

3

CLB

8 4 8 4

Quad

Single

Double

Long

DirectConnect

DirectConnect

Quad Long GlobalClock

Long Double Single GlobalClock

CarryChain

Long12 4 4

Courtesy XilinxAdapted from Digital Integrated Circuits (2nd Edition). Copyright 2002 J. Rabaey et al."

45

RAM-based FPGA

Xilinx XC4000ex

Courtesy XilinxAdapted from Digital Integrated Circuits (2nd Edition). Copyright 2002 J. Rabaey et al."

46

Heterogeneous Programmable Platforms

Xilinx Vertex-II Pro

Courtesy Xilinx

High-speed I/O

Embedded PowerPcEmbedded memories

Hardwired multipliers

FPGA Fabric


SOC as a heterogeneous computing substrate

ASIC

DSPCode

System Bus

Proc.Code

CODEC

Analoginterface

ProgrammableProcessor CoreMemory Interface

Host/Bus InterfaceProgrammable

DSP CoreMemory Interface

Host/Bus Interface

User interface

Multi-ported memory

Real time Operating System

Code

MicroprocessorCore

ASIC

Controller process

BUSCNTL

SERIAL I/O

ASICASIC

DSPCodeDSPCode

System Bus

Proc.CodeProc.Code

CODEC

Analoginterface

ProgrammableProcessor CoreMemory Interface

Host/Bus InterfaceProgrammableProcessor CoreMemory Interface





Host/Bus Interface

User interface

User interface

Multi-ported memoryMulti-ported memory


Code

MicroprocessorCore



CodeCode

MicroprocessorCore

ASICASIC

Controller process

BUSCNTL

SERIAL I/O

Experimental Side of Putting Things Together

Design

Goal of design is to take an ‘idea’ and build something that performance a certain functionSuch ‘idea’ to ‘implementation’ never happen directly

We go through ‘models’ that allow us to reason about propertiesMay also be used by implementers to explore alternatives for cost, performance

MODELS are key to formalization of the designAnd its process.

Model of Computation

A ‘model’ is an abstraction of a ‘description’(Sometimes, a model is also used as a replica of a ‘description’)

This abstraction is defined using some ‘terms’If the terms are graphical graphical modelIf the terms are mathematical formal modelGenerally, terms and their relationships are devised to allow syntactical support for expressing important conceptsIf done right, a MOC

supports important concepts of an application domain through use of right terms is clear and unambiguous to allow anyone to replicate/simulate intended behavioris compositional: compositions can be validated with less effortthan ab initio description

Compositional View of SOCs:Model of Computation

A system consists of componentsImportant questions to ask when dealing with components

What is a component? (Component ontology)States? Processes? Threads? Differential equations? Constraints? Objects? …

What knowledge do components share? (Epistemology)Time? Name spaces? Signals? State?

How do components communicate? (Protocols)Events? Rendezvous? Message Passing? CT Signals? Streams? Method Calls? …

What do components communicate? (Lexicon)Objects? Transfer of control? Data structures? Strings?...

A MOC makes it easier to reason through these questionsStart with a model of a machine, define its behavior (as

Characteristics of Common MOCs

Finite State MachinesState is summary of past, Finite number of statesNo concurrency, no explicit time specification

Data-FlowPartial order of actions/eventsConcurrency, determinate, support streams (data, computation)

Discrete-event modelsGlobal notion of time, causality

Finite State Machines (FSMs)

Functional decomposition into states of operationUseful for control functions, protocols

Properties of FSMsGood for specifying sequential control.Not Turing complete.

More amenable to formal analysis.Typical domains of application

Control-intensive tasks.Protocols (Telecom, cache-coherency, bus, ...)

Many variants of the formulationDiffer in communication, determinism, ...

ALARM

OFF5_SECONDS_UP => ALARM_ON

WAIT

KEY_OFF or BELT_ON

KEY_ON => START_TIMER

10_SECONDS_UP or BELT_ON or KEY_OFF => ALARM_OFF

Informal SpecificationIf the driver

turns on the key, and does not fasten the seat belt within 5 seconds

then sound the alarm for 5 seconds, or until the driver fastens the seat beltor until the driver turns off the key

No explicit condition => implicit self-loop in the current state

FSM Example: Seat Belt Alarm Control

FSM = (Inputs, Outputs, States, InitialState, NextState, Outs)

Inputs = {KEY_ON, KEY_OFF, BELT_ON, BELT_OFF, 5_SECONDS_UP, 10_SECONDS_UP}Outputs = {START_TIMER, ALARM_ON, ALARM_OFF}States = {OFF, WAIT, ALARM}

InitialState = OFFNextState: CurrentState, Inputs -> NextState

e.g., NextState(WAIT, {KEY_OFF}) = OFFAll inputs other than KEY_OFF are implicitly absent

Outs (function): CurrentState, Inputs -> Outputs

e g Outs(OFF {KEY ON}) =

ALARM

OFF5_SECONDS_UP => ALARM_ON

WAIT

KEY_OFF or BELT_ON

KEY_ON => START_TIMER

10_SECONDS_UP or BELT_ON or KEY_OFF => ALARM_OFF

Finite State Machine: Example + Definition

NextState: 2Inputs x S -> SSet of all subsets of I

Outs: 2Inputs x S -> 2Outputs

A finite state machine is said to be non-deterministic when The NextState and Output functions may be RELATIONs (instead of functions).NextState(WAIT, {KEY_OFF, END_TIMER_5})={{OFF},{ALARM}}

Non-determinism can be user to modelunspecified behavior

incomplete specificationunknown behavior

e.g., the environment modelDriver can be modeled as single state FSM with outputs {KEY_ON, KEY_OFF, BELT_ON}

abstraction(the abstraction may result in insufficient detail to identify

i l di ti i h bl it ti )

Non-deterministic Finite State Machines

Concurrency and FSM

Significant model change: treat it as a ‘collection’Fundamental assumption: all FSMs change states together (synchronicity)

System state is a cartesian productState space can be reduced by constrained compositions

E.g., sequential composition: output of one machine is input of another

A cleaner way to extend FSM model?Hierarchy

Discrete Event Models

Action, EventsNotion of global time

Though it is not fundamental: time progress can be captured by ‘special’ events

Events can happen anytime asynchronouslyA system consists of components with input events and output events

Also, referred to as ‘primary events’.

Component is evaluated in response to input events Evaluation leads to events at the output

A discrete event simulator is a program that specifies how components are evaluated

Components at a time (‘clock-driven’)Event at a time (‘event-driven’)

Reactive Systems“React” to events

e.g., in the external environment, other subsystemsSuited for modeling “non-terminating” interactions

e.g., operating systems, interrupt handlers, process control systems.Often subject to external timing constraints

“real-time”Synchronous Reactive Systems

Synchrony associates ‘clock’ to a modelAll ‘synchronous events’ happen simultaneously

Clock is a ‘simplifcation’ or abstraction of time in modelsBetween clocks, any amount of time can pass

Reactive (Real-time) Systems

60

Four useful MOCs

• Discrete Event (DE)– Timed models, suitable for modeling digital hardware– But can be very general (define what is an event and what happens to it)

• Finite State Machines– Variants and extensions: StateCharts, StarCharts

• Synchronous Reactive Models– Synchrony assumption useful for safety critical embedded systems

(instantaneous reactions)• (Convert timing relations to causal ordering)

– A program is logically correct if it is deterministic and reactive– Verifying that a program is causal is a challenge

• Want one and only solution for each configuration of inputs – Assume “constructive causality” to make it work

• Still a lot better than multi-level time (delta) models• Dataflow Process Networks

– Signal processing applications

61

Compositional Correctness

• Build “Complete” System Models– That include the application and system software– Adapt, control and debug applications– Explore the full potential of SOC architectural platforms

• e.g., by exploring applications, networking and communication subsystems together

• Composition challenges– Language support for multiple MOCs not enough– Model composability may not be guaranteed

• E.g., composition of synchronous models may not be closed• Like connecting two FSMs can lead to combinational cycles

– solutions like: delta steps (VHDL), acyclic composition (Lustre), reactions as fixed points (Esterel

62

Going Across MOC: Ptolemy Approach

• Encapsulate each description in a MOC in a “domain”• Inter-domain simulations achieved through domain

encapsulation– Define semantics of every such encapsulation carefully,

conservatively (and yet with some efficiency)• The “event horizon”

– Couple timed, untimed domains

63

Network Architecture Modeling: NS2

• Developed under the Virtual Internet Testbed (VINT) project (UCB, LBL, USC/ISI, Xerox PARC)

• Captures network nodes, topology and provides efficient event driven simulations with a number of “schedulers”

• Interpreted interface for– network configuration, simulation setup– using existing simulation kernel objects such as predefined

network links• Simulation model in C++ for

– packet processing– changing models of existing simulation kernel classes, e.g.,

using a special queuing discipline.

64

NS2 Simulations

65

A 4-node system with 2 “agents”, a traffic generator

n0UDP

n1TCP

n2 n3Sink

ftp

set ns [new Simulator]set f [open out.tr w]$ns trace-all $fset n0 {$ns node}set n1 {$ns node}set n2 {$ns node}set n3 {$ns node}$ns duplex-link $no $n2 5Mb 2ms DropTail$ns duplex-link $n1 $n2 5Mb 2ms DropTail$ns duplex-link $n2 $n3 1.5Mb 10ms DropTailset udp0 [newagent/UDP]$ns attach-agent $n0 $udp0set cbr0 [newapplication/Traffic/CBR]$cbr0 attach-agent $udp0..$ns at 3.0 “finish”proc finish () {

…}$ns run

• “Agents” are network endpoints where network-layer packets are constructed or consumed.

66

NS2 Usage: LAN nodes

• LAN and wireless links are inherently different from PTP links due to sharing and contention properties of LANs

– a network consisting of PTP links alone can not capture LAN contention properties

– a special node is provided to specify LANs• LanNode captures functionality of three lowest layers in the

protocol stack, namely: link, MAC and physical layers.– Specifies objects to be created for LL, INTF, MAC and Physical

channels.– Example:$ns make-lan <nodelist> <bw> <delay> <LL> <ifq> <MAC> <channel> <phy>$ns make-lan “$n1 $n2” $bw $delay LL queue/DropTail Mac/CSMA/CD.

– Creates a LAN with basic link-layer, drop-tail queue and CSMA/CD medium access control.

n1 n2

n3

n1 n2

n3

LAN

The LAN node collects all the objects shared on the

LAN.

67

node1

Q

LL

MAC

node2

Q

LL

MAC

node3

Q

LL

MAC

Channel MAC classifier

LL

MAC

Phy

Channel object simulates the shared medium and supports the medium access mechanisms

of the MAC objects on the sending side.

On the receiving side, MAC classifier is responsible for delivering and optionally replicating packets to the receiving MAC

objects.

Network Stack simulation for LAN nodes in ns

Objects used in LAN nodes. Each of the underlying classes can be specialized for a given simulation.

68

Putting things together…

Source: Virtio Corp.

ASIC HardwareNetwork

Processor(s) and MemoriesSystem Software: OS, Middleware, Application Software

69

Time Granularity in ModelsA. "Specification model" "Untimed functioal models"

B. "Component-assembly model" "Architecture model" "Timed functonal model"

C. "Bus-arbitration model" "Transaction model"

D. "Bus-functional model" "Communicatin model" "Behavior level model"

E. "Cycle-accurate computationmodel"

F. "Implementation model" "Register transfer model"

Computation

Communication

A B

C

D F

Un-timed

Approximate-timed

Cycle-timed

Un-timed

Approximate-timed E

Cycle-timed

• Models B, C, D and E could be classified as TLMs.

Source: Daniel Gajski, UC Irvine.

70

Hardware-software co-simulation

• Verification of the functionality of a system consisting of both hardware and software (as early as possible in the design cycle).

ProcessorModel

CustomHardware

ModelCommunication

• BFM• ISA• CAM• TAM

• Functional• Behavioral• RTL• Gate• Transistor

• Tightly coupled• Loosely coupled• One process• Multi-process

71

Processor Models

• Four types of models– Bus-functional models– Instruction-set models– Cycle-accurate models– Timing accurate models

BFM

ISM

72

Bus-functional Models

• Can only execute bus transactions• Can be used to check how peripherals interact with the processor bus• Available in different degrees of timing accuracy

– Cycle-accurate– Phase-accurate– Full timing (nanosecond) accurate

• Very popular in hardware design

BFM

CLK

ADDRESS

CE

DATA

R/W

Rea

d fr

om 0

xff0

0

73

Instruction-set (ISA) Models

• Basic ISA Model– Model only the effect of

instruction execution on registers and memory

– Not processor pipeline– Fast, used in embedded

software models• Cylcle-accurate ISA

– Model the processor pipeline and instruction execution in a cycle-accurate manner

– Provides accurate cycle counts for instruction execution

– 1.2-5X slower

Fetch ExecuteDecode

Register File

Memorymov r0, r1add r0, r2, r3st r0, (r5)

74

Processor Models• ISA Processor Model

– ISA Model + Cycle-accurate BFM– Cycle accurate bus transactions but not cycle

accurate instruction execution– Fastest useful processor model

• Cycle-accurate Processor Model– Cycle-accurate ISA + Cycle-accurate BFM– Cycle accurate instruction execution and bus

transactions– Slower than ISA processor model but still popular.

BFM

ISM

75

Timing-accurate Models

• Correctly models the processor behavior at the nanosecond accurate level

• Is usually generated from a gate-level netlist of the processor

• Slow (could be 3 to 5 orders of magnitude slower than cycle-accurate processor models)

• Seldom used

76

Typical Usage Models

• System architects looking at hardware/software tradeoffs• ASIC developers wanting a fast and easy way to test out the

hardware running actual code• Software developers testing H/W drivers and RTOS on

hardware (HDL) models• Software developers testing application code with an RTOS

on the “real” hardware (i.e. evaluation board)• Distributed application developers

– SensorSIM, TOSSIM

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