cse 291 winter 2009 the fpga ecosystem rajesh gupta university of california, san diego

CSE 291 Winter 2009

The FPGA Ecosystem

Rajesh Gupta

University of California, San Diego

Moore’s Law

40048008

80808085

8086286

386486 Pentium ® proc

1970 1980 1990 2000 2010

~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

1970 1980 1990 2000 2010

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

Pentium ® proc486

38628680868085

80084004

1970 1980 1990 2000 2010

2X every 2 years

Courtesy, Intel

P6Pentium ® proc

3862868086

80858080

80084004

1971 1974 1978 1985 1992 2000Year

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

Source: Ken Yang, UCLA

2007 0.065 micron

6.7 GHz on chip clock 9 wiring levels 600-3000 pins Vdd=0.7-1.1V

3.5W / 104W / 190W DRAM:

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

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

Design Abstraction Levels

SYSTEM

CIRCUIT

VoutVin

CIRCUIT

VoutVin

MODULE

DEVICE

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

Speed Power Cost

High Low Volume

Many Implementation Choices

• Microprocessors

• Domain-specific processors– DSP

– Network processors

– Microcontrollers

• ASIPs

• Reconfigurable SoC

• FPGA

• Gate-array

• ASIC

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 = 0 for i =1 to …

Control logic and State register

Datamemory

Single-purpose (“hardware”)

DatapathController

Control logic

State register

Datamemory

Registerfile

GeneralALU

DatapathController

Program memory

Assembly code for:

Control logic and

State register

Datamemory

General-purpose (“software”)

total = 0for i = 1 to N loop total += M[i]end loop

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

Registerfile

GeneralALU

DatapathController

Program memory

Assembly code for:

Control logic and

State register

Datamemory

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

Registers

CustomALU

DatapathController

Program memory

Assembly code for:

Control logic and

State register

Datamemory

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

E.g. ASIC

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

ASIC FeaturesArea: 4.6 mm x 5.1 mm

Speed: 20 MHz @ 10 Mcps

Technology: HP 0.5 m

Power: 16 mW - 120 mW (mode dependent) @ 20 MHz, 3.3 V

Avg. Acquisition Time: 10 s to 300 s

The Implementation Choice is Important

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.

Map from Behavior to Architecture

[Vincentelli]

Four Phases in Creating a Chip

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

Transition to Automation and Regular Structures

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

Cell-based Design (or standard cells)

Routing channel requirements arereduced by presenceof more interconnectlayers

Functionalmodule(RAM,multiplier,…)

Routingchannel

Logic cellFeedthrough cellR

Standard Cell - Example

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

Automatic Cell Generation

Courtesy Acadabra

Initial transistorgeometries

Placedtransistors

Routedcell

Compactedcell

Finishedcell

MacroModules

25632 (or 8192 bit) SRAMGenerated by hard-macro module generator

“Soft” MacroModules

“Intellectual Property”

A Protocol Processor for Wireless

Semicustom Design Flow

HDLHDL

Logic SynthesisLogic Synthesis

FloorplanningFloorplanning

PlacementPlacement

RoutingRouting

Tape-out

Circuit ExtractionCircuit Extraction

Pre-Layout Simulation

Post-Layout Simulation

StructuralStructural

PhysicalPhysical

BehavioralBehavioralDesign Capture

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

Gate Array — Sea-of-gates

rows of

routing channel

uncommitted

polysilicon

possiblecontact

In1 In2 In3 In4

UncommitedCell

CommittedCell(4-input NOR)

Sea-of-gate Primitive Cells

Oxide-isolation

Using oxide-isolation Using gate-isolation

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

Antifuse

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

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

Array-Based Programmable Logic

PLA PROM PAL

I 5 I 4

I 3 I 2 I 1 I 0

O1O2O3

Programmable AND array

ProgrammableOR array I5 I4

I3 I2 I1 I0

O1O2O3

Programmable AND array

Fixed OR array

Indicates programmable connection

Indicates fixed connection

I3 I2 I1 I0

O1O2O3

Fixed AND array

ProgrammableOR array

Programming a PROM

1 X 2 X 1 X 0

f1NANA

: programmed node

2-input mux as programmable logic block

Configuration

A 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

Logic Cell of Actel Fuse-Based FPGA

Look-up Table Based Logic Cell

ln1 ln2

mory In Out

LUT-Based Logic Cell

Courtesy Xilinx

C1....C4

xxxxxx

Logicfunction

xxxxxx

xxxxxxxx

xxxx xxxx xxxx

Bitscontrol

Multiplexer Controlledby Configuration Program

xxx xx

xxxxxx

Xilinx 4000 Series

Array-Based Programmable Wiring

Input/output pinProgrammed interconnection

InterconnectPoint

Horizontaltracks

Vertical tracks

Mesh-based Interconnect Network

Switch Box

Connect Box

InterconnectPoint

Transistor Implementation of Mesh

Hierarchical Mesh Network

Use overlayed meshto support longer connections

Reduced fanout and reduced resistance

EPLD Block Diagram

MacrocellPrimary inputs

Altera MAX

Altera MAX Interconnect Architecture

row channelcolumn channel

Courtesy Altera

Array-based(MAX 3000-7000)

Mesh-based(MAX 9000)

Field-Programmable Gate ArraysFuse-based

I/O Buffers

P rogram/Test/Diag nostics

I/O Buffers

Vertical ro utes

Rows o f logic m odule s

Routing channels

Standard-cell likefloorplan

Xilinx 4000 Interconnect Architecture

8 4 8 4

Single

Double

DirectConnect

Quad Long GlobalClock

Long Double Single GlobalClock

CarryChain

12 4 4

RAM-based FPGA

Xilinx XC4000ex

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

DSPCode

System Bus

Proc.Code

Analoginterface

ProgrammableProcessor CoreMemory Interface

Host/Bus InterfaceProgrammable

DSP CoreMemory Interface

Host/Bus Interface

User interface

Multi-ported memory

Real time Operating System

MicroprocessorCore

Controller process

BUSCNTL

SERIAL I/O

ASICASIC

DSPCodeDSPCode

System Bus

Proc.CodeProc.Code

Analoginterface

ProgrammableProcessor CoreMemory Interface

Host/Bus InterfaceProgrammableProcessor CoreMemory Interface

Host/Bus Interface

User interface

Multi-ported memoryMulti-ported memory

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 function

Such ‘idea’ to ‘implementation’ never happen directly We go through ‘models’ that allow us to reason about properties May also be used by implementers to explore alternatives for

cost, performance

MODELS are key to formalization of the design And 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 model If the terms are mathematical formal model Generally, terms and their relationships are devised to allow syntactical

support for expressing important concepts If 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 behavior

is compositional: compositions can be validated with less effort than ab initio description

Compositional View of SOCs:Model of Computation A system consists of components Important 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 questions Start with a model of a machine, define its behavior (as operational semantics)

Characteristics of Common MOCs Finite State Machines

State is summary of past, Finite number of states No concurrency, no explicit time specification

Data-Flow Partial order of actions/events Concurrency, determinate, support streams (data, computation)

Discrete-event models Global notion of time, causality

Finite State Machines (FSMs)

Functional decomposition into states of operation Useful for control functions, protocols

Properties of FSMs Good 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 formulation Differ in communication, determinism, ...

OFF5_SECONDS_UP => ALARM_ON

KEY_OFF or BELT_ON

KEY_ON => START_TIMER

10_SECONDS_UP or BELT_ON or KEY_OFF => ALARM_OFF

Informal Specification If 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 belt or 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 = OFF NextState: CurrentState, Inputs -> NextState

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

Outs (function): CurrentState, Inputs -> Outputs e.g., Outs(OFF, {KEY_ON}) = START_TIMER

OFF5_SECONDS_UP => ALARM_ON

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 -> S Set 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 model

unspecified behavior incomplete specification

unknown behavior e.g., the environment model Driver 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 previously distinguishable situations)

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 product State 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, Events Notion of global time

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

Events can happen anytime asynchronously A 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 subsystems Suited 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 model All ‘synchronous events’ happen simultaneously

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

Reactive (Real-time) Systems

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

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

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

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.

NS2 Simulations

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

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.

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.

The LAN node collects all the objects shared

on the LAN.

Channel MAC classifier

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.

Putting things together…

Source: Virtio Corp.

ASIC HardwareNetwork Processor(s) and Memories

System Software: OS, Middleware, Application Software

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

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.

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

Processor Models

• Four types of models– Bus-functional models

– Instruction-set models

– Cycle-accurate models

– Timing accurate models

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

ADDRESS

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)

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.

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

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

cse 291 winter 2009 the fpga ecosystem rajesh gupta university of california, san diego

model of computationa

model of computationa

inputs outputse

inputs nextstatee

offall inputs

use of right terms

states of operationuseful

support streams data

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