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Embedded Systems

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TMS320C6x Datapath

Cross Paths40-bit Write Paths (8 MSBs)40-bit Read Paths/Store Paths

M2

Registers B0 - B15

L2S2

DDATA_I2(load data)

S2D

DL

SLSL D DLS2

S1

S1

S2D2

DS1

S2

D

M1

Registers A0 - A15

L1 S1S1 S2

DDL

SL SLDDL S2

S1

S1

S2 D1

D S1

S2

D

DDATA_O2(store data)DADR2

(address)DADR1

(address)

DDATA_I1(load data)

DDATA_O1(store data)

2X1X

2 Data Paths 8 Functional Units

Orthogonal/Independent 6 Arithmetic Units 2 Multipliers

Control Independent Up to 8 32-bit Instructions in parallel

Registers 2 Files 32, 32-bit Registers Total

Cross paths (1X, 2X)

L-Unit (L1, L2) 40-bit Integer ALU Comparisons Bit Counting Normalization

S-Unit (S1, S2) 32-bit ALU 40-bit Shifter Bitfield Operations Branching

M-Unit (M1, M2) 16 x 16 -> 32

D-Unit (D1, D2) 32-bit Add/Subtract Address Calculations

S1

REVIEW

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Overview XILINX FPGA

• All Xilinx FPGAs contain the same basic resources– Slices grouped into Configurable Logic Blocks (CLBs)

• Contain combinatorial logic and register resources

– IOBs• Interface between the FPGA and the outside world

– Programmable interconnect

– Other resources• Memory• Multipliers• Global clock buffers• Boundary scan logic

REVIEW

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Embedded Processors in FPGAs

Hard Core EP is a dedicated physical component of the chip

separate from the programmable logic E.g. Xilinx Virtex families (PowerPC 405)

Soft Core Embedded processor is also a synthesized to the FPGA to th

programmable logic on the chip E.g. Altera (NIOS), Xilinx (MicroBlaze)

REVIEW

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Partial Reconfiguration Technology and Benefits

Partial Reconfiguration enables: System Flexibility

• Perform more functions while maintaining communication links

Size and Cost Reduction• Time-multiplex the hardware

to require a smaller FPGA

Power Reduction• Shut down power-hungry tasks

when not needed

REVIEW

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Embedded System Hardware

Embedded system hardware is frequently used in a loop(“hardware in a loop“):

cyber-physical systems

REVIEW

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Communication- Requirements -

Real-time behavior Efficient, economical

(e.g. centralized power supply) Appropriate bandwidth and communication delay Robustness Fault tolerance Maintainability Diagnosability Security Safety

REVIEW

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Memory

For the memory, efficiency is again a concern: speed (latency and throughput); predictable timing energy efficiency size cost other attributes (volatile vs. persistent, etc)

REVIEW

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Memory hierarchyRegister, internalCaches in CPU

External Caches (SRAM)

Main Memory (DRAM)

Disk Storage (Magnetics)

Tape Units (Magnetics)

Ene

rgy

Con

sum

ptio

n

CO

STS

Level 0

Level 1

Level 2

Level 3

Level 4

Capacity

“Small is beautiful”(in terms of energy consumption, access times, size)

REVIEW

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REVIEW

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REVIEW

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Architecture Synthesis

HW/SW Codesign

Power Aware Computing

3.2.2011 Lecture by Bernd Finkbeiner, Head of Reactive Systems Group at Saarland University(http://react.cs.uni-sb.de/

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Architecture Synthesis

tasks: allocation (determine the necessary hardware resources) scheduling (determine the timing of individual operations) binding (determine relation between individual operations of the

algorithm and hardware resources)

Classification of synthesis algorithms

Synthesis methods can often be applied independently of granularity

Design a hardware architecture that efficientlyexecutes a given algorithm.

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Synthesis in Temporal Domain

Scheduling and binding can be done in different orders or together

Schedule: Mapping of operations to time slots (cycles) A scheduled sequencing graph is a labeled graph

[©Gupta]

+

NOP

+ <-

-NOP

1

23

4

+

NOP

+

<-

-

NOP

1

23

4

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Schedule in Spatial Domain

Resource sharing More than one operation bound to same resource Serialized operations

[©Gupta]

+

NOP

+ <

-

-

NOP

1

2

3

4

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BASICS

Source: Teich: Dig. HW/SW Systeme;Thiele ETHZ

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Models

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Modelsint diffeq(int x, int y, int u, int dx, int a)

{ int x1, u1, y1;while ( x < a ) {

x1 = x + dx;u1 = u - (3 * x * u * dx) - (3 * y * dx);y1 = y + u * dx;x = x1; u = u1; y = y1;

}return y;

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Allocation and Binding

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Scheduling

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Schedule

+

NOP

+ <-

-NOP

1

23

4

v2v1

v3

v4

v5

vn

v6

v7

v8

v9

v10

v11

L = (vn) - (v0) = 4

(v1) = (v2) … = 1

(v0) =

(v5) = 4

(vn) = 5

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+

NOP

+ <

-

-

NOP

1

2

3

4

Binding

Example ((r1) = 4, (r2) = 2):(v1) = r1, (v1) = 1

v2v1

v3

v4

v5

vn

v6

v7

v8

v9

v10

v11

(v2) = r2, (v2) = 1

(v3) = r1, (v3) = 2

(v6) = r1, (v3) = 3

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As soon as possible (ASAP) scheduling

ASAP: All tasks are scheduled as early as possible

Loop over (integer) time steps: Compute the set of unscheduled tasks for which all

predecessors have finished their computation

Schedule these tasks to start at the current time step.

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ASAP Schedules

+

NOP

+ <-

-NOP

1

23

4

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As-late-as-possible (ALAP) scheduling

ALAP: All tasks are scheduled as late as possible

Start at last time step*:Schedule tasks with no successors and tasks for which

all successors have already been scheduled.

* Generate a list, starting at its end

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ALAP Schedules

+

NOP

+ <-

-NOP

1

23

4

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Motivation Interface design. Control over operation start time.

Constraints Upper/lower bounds on start-time difference of any operation pair.

Minimum timing constraints between two operations An operation follows another by at least a number of prescribed time

steps

Maximum timing constraints between two operations An operation follows another by at most a number of prescribed time

steps

Scheduling under Detailed Timing Constraints

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Example

Circuit reads data from a bus, performs computation, writes result back on the bus.

Bus interface constraint: data written three cycles after read.

Minimum and maximum constraint of 3 cycles between read and write operations.

Scheduling under Detailed Timing Constraints

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Constraint graph model Start from a sequencing graph

Model delays as weights on edges

Add forward edges for minimum constraints

Add backward edges for maximum constraints

Add this edge for max constraint

Add this edge for min constraint

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Weighted Constraint Graph

Source: Thiele, ETHZ

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Weighted Constraint Graph

Example: w(v1) = w(v3) = 2 w(v2) = w(v4) = 1

Source: Thiele, ETHZ

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Mul delay = 2ADD delay =1

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(Resource constrained)List Scheduling

List scheduling: extension of ALAP/ASAP methodPreparation:

Greedy strategy (does NOT guarantee optimum solution) Topological sort of task graph G=(V,E) Computation of priority of each task:

Possible priorities u:• Number of successors• Longest path• Mobility = (ALAP schedule)- (ASAP schedule)

– Defined for each operation– Zero mobility implies that an operation can be started only

at one given time step– Mobility greater than 0 measures span of time interval in

which an operation may start Slack on the start time.

Source: Teich: Dig. HW/SW Systeme

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Mobility as a priority function

Mobility is not very precise

=1

=2

=3

=4

=5

=1

=2

=3

=4

=5

a

b c d e f g

h i j

k l m

n

z

=0a

b c d e f g

h i j

k l m

n

z

=0

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Algorithm

List(G(V,E), B, u){i :=0;

repeat {Compute set of candidate tasks Ai ;Compute set of not terminated tasks Gi ;Select Si Ai of maximum priority r such that| Si | + | Gi | ≤ B (*resource constraint*)

foreach (vj Si): (vj):=i; (*set start time*)i := i +1;

}until (all nodes are scheduled);return ();

} Complexity: O(|V|)

may be repeated

for different

task/ processor classes

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Example

Assuming B =2, unit execution time and u : path length

u(a)= u(b)=4u(c)= u(f)=3u(d)= u(g)= u(h)= u(j)=2u(e)= u(i)= u(k)=1 i : Gi =0

a b

i

c f

g

h j

k

d

ea b

c

f

g

d

e

h

i

j

k

=0

=1

=2

=3

=4

=5

Modified example based on J. Teich

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does NOT guarantee optimum solution e.g.

List Scheduling

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Integer linear programming models

Ingredients: Cost function Constraints

Involving linear expressions of integer variables from a set X

Def.: The problem of minimizing (1) subject to the constraints (2) is called an integer linear programming (ILP) problem.

If all xi are constrained to be either 0 or 1, the IP problem said to be a 0/1 integer linear programming problem.

Cost function )1(, NxRaxaC iXx

iiii

with

Constraints: )2(,: ,, RcbcxbJjXx

jjijijii

with

- 39 -CS - ES

Example

321 465 xxxC

}1,0{,,2

321

321

xxxxxx

Optimal

C

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Remarks on integer programming

Integer programming is NP-complete

Running times depend exponentially on problem size,but problems of >1000 vars solvable with good solver (depending on the size and structure of the problem)

ILP/LP models good starting point for modeling, even if heuristics are used in the end.

Solvers: lp_solve (public), CPLEX (commercial), …

- 41 -CS - ES

Minimize latency given constraints on area orthe resources (ML-RCS)

Use binary decision variables i = 0, 1, ..., n l = 1, 2, ..., ’+1 ’ given upper-bound on latency xil = 1 if operation i starts at step l, 0 otherwise.

Set of linear inequalities (constraints),and an objective function (min latency)

ILP Formulation of ML-RCS[Mic94] p.198, Kastner, UC S. Barbara

- 42 -CS - ES

Observation

ti = start time of op i.

is op vi (still) executing at step l?

ILP Formulation of ML-RCS

))(),((

0

iLii

Si

Li

Siil

vALAPtvASAPt

tlandtlforx

ill

i xlt .

11

l

dlmim

i

x?

- 43 -CS - ES

Start Time vs. Execution Time For each operation vi , only one start time If di=1, then the following questions are the

same: Does operation vi start at step l? Is operation vi running at step l?

But if di>1, then the two questions should be formulated as: Does operation vi start at step l?

• Does xil = 1 hold?

Is operation vi running at step l?• Does the following hold?

11

l

dlmim

i

x ?

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Operation vi Still Running at Step l ? Is v9 running at step 6? Is x9,6 + x9,5 + x9,4 = 1 ?

Note: Only one (if any) of the above three cases can happen To meet resource constraints, we have to ask the

same question for ALL steps, and ALL operations of that type

v9

456

x9,4=1

v9

456

x9,5=1

v9

456

x9,6=1

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Operation vi Still Running at Step l ?

Is vi running at step l ? Is xi,l + xi,l-1 + ... + xi,l-di+1 = 1 ?

vi

ll-1

l-di+1...

xi,l-di+1=1

vill-1

l-di+1

...

xi,l-1=1

vill-1

l-di+1

...

xi,l=1

. . .

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Constraints: Unique start times:

Sequencing (dependency) relations must be satisfied

Resource constraints

Objective: min cTt. t =start times vector, c =cost weight

ILP Formulation of ML-RCS (cont.)

l

il nix ,,1,0,1

jl

jll

ilijjji dxlxlEvvdtt ..),(

1,,1,,,1,)(: 1

lnkax reskkvTi

l

dlmim

i i

- 47 -CS - ES

ILP Example Assume = 4 First, perform ASAP and ALAP

(we can write the ILP without ASAP and ALAP, but using ASAP and ALAP will simplify the inequalities)

+

NOP

+ <-

-NOP

1

23

4

+

NOP

+ <-

-NOP

1

23

4

v2v1

v3

v4

v5

vn

v6

v7

v8

v9

v10

v11

v2v1

v3

v4

v5

vn

v6

v7 v8

v9

v10

v11

- 48 -CS - ES

ILP Example: Unique Start Times Constraint Without using ASAP

and ALAP values: Using ASAP and

ALAP:

...1

1

4,23,22,21,2

4,13,12,11,1

xxxxxxxx

....11

11

11111

4,93,92,9

3,82,81,8

3,72,7

2,61,6

4,5

3,4

2,3

1,2

1,1

xxxxxx

xxxx

xxxxx

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ILP Example: Dependency Constraints Using ASAP and ALAP, the non-trivial inequalities are:

(assuming unit delay for + and *)

...01.3.2.401.3.2.4.3.201.3.2.4.3.201.2.3.2

3,72,74,5

3,102,101,104,113,112,11

3,82,81,84,93,92,9

2,61,63,72,7

xxxxxxxxxxxxxxxxxxx

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ILP Example: Resource Constraints Resource constraints (assuming 2 adders and 2 multipliers)

Objective: Min Xn,1 + 2Xn,2 +3Xn,3 +4Xn,4

2222222

4,114,94,5

3,113,103,93,4

2,112,102,9

1,10

3,83,7

2,82,72,62,3

1,81,61,21,1

xxxxxxxxxxxxxxxxxxxxx

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Result is different from both ALAP and ASAP schedules

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(Time constrained)Force-directed scheduling

Goal: balanced utilization of resourcesBased on spring modelOriginally proposed for high-level synthesisForce Used as a priority function Related to concurrency – sort operations for least

force Mechanical analogy: Force = constant x displacement

• Constant = operation-type distribution• Displacement = change in probability

* [Pierre G. Paulin, J.P. Knight, Force-directed scheduling in automatic data path synthesis, Design Automation Conference (DAC), 1987, S. 195-202]

© ACM

- 53 -CS - ES

Force-Directed Scheduling

The Force-Directed Scheduling approach reduces the amount of:

• Functional Units• Registers• Interconnect

This is achieved by balancing the concurrency of operations to ensure a high utilization of each unit.

- 54 -CS - ES

Next: computation of “forces”

Direct forces push each task into the direction of lowervalues of D(i).

Impact of direct forces on dependent tasks taken into account by indirect forces

Balanced resource usage smallest forces For our simple example and time constraint=6:

result = ALAP schedule0

1

2

3

4

5

2 31 4 5

i

=1

=2

=3

=4

=5

a

b c d e f g

h i j

k l m

n

z

=0

- 55 -CS - ES

1.Compute time frames R(j)2. Compute “probability“ P(j,i) of assignment j i

R(j)={ASAP-control step … ALAP-control step}

if0 otherwise Fixed Free

- 56 -CS - ES

3. Compute “distribution” D(i)(# Operations in control step i)

P(j,i) D(i)

- 57 -CS - ES

Example

+

NOP

+ <-

-NOP

1

23

4

0)4(

83.031

21)3(

33.231

21

211)2(

83.231

2111)1(

mult

mult

mult

mult

q

q

q

q

2.83

2.33

.83

66.131

311)4(

231

31

311)3(

131

31

31)2(

33.031)1(

add

add

add

add

q

q

q

q

0

1

2

1.66

0.33

- 58 -CS - ES

Scheduling – An example

b

ti hiDGjDGjForce )()()(

A metric called force is introduced. The force is used to optimize the utilization of units. A high positive force value indicates a poor utilization.

Step 3 : Calculate the force (a new metric)

- 59 -CS - ES

Scheduling – An example

2

1 2)()1()1(

i

iDGDGForce

Step 3 : Calculate the force (a new metric)With the operation x’ in control-step 1.

DG(1) = 2.833 DG(3) = 0.833DG(2) = 2.333 DG(4) = 0

25.02

333.2833.2833.2

Poor utilization

- 60 -CS - ES

Scheduling – An example

3

2

2

1 2)()3(

2)()2()2(

ii

iDGDGiDGDGForce

Step 3 : Calculate the force (a new metric)With the operation x’ in control-step 2. (x’’ must be in control-step 3)

DG(1) = 2.833 DG(3) = 0.833DG(2) = 2.333 DG(4) = 0

12

333.2833.0833.02

333.2833.2333.2

Good utilization

Direct force (calculated as before)

Indirect force (on x’’ in control-step 3)

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Scheduling – An example

By repeatedly assigning operations to various control-steps and calculating the force associated with the choice several force values will be available.

The Force-directed scheduling algorithm chooses the assignment with the lowest force value, which also balances the concurrency of operations most efficiently.

- 62 -CS - ES

Overall approach

procedure forceDirectedScheduling;begin

AsapScheduling;AlapScheduling;while not all tasks scheduled do

beginselect task T with smallest total force;schedule task T at time step minimizing forces;recompute forces;

end;end

May be repeated

for different

task/ processor classes

Not sufficient for today's complex, heterogeneous hardware platforms

- 63 -CS - ES

Force-Directed Scheduling

The Force-Directed Scheduling approach reduces the amount of:

• Functional Units• Registers• Interconnect

By introducing Registers and Interconnect as storage operations, the force is calcuted for these as well.

- 64 -CS - ES

Force-Directed Scheduling

- 65 -CS - ES

Architecture Synthesis

HW/SW Codesign

Power Aware Computing

3.2.2011 Lecture by Bernd Finkbeiner, Head of Reactive Systems Group at Saarland University (http://react.cs.uni-sb.de/

- 66 -CS - ES

Codesign Definition and Key Concepts

Codesign The meeting of system-level objectives by exploiting the

trade-offs between hardware and software in a system through their concurrent design

Key concepts Concurrent: hardware and software developed at the same

time on parallel paths Integrated: interaction between hardware and software

development to produce design meeting performance criteria and functional specs

- 67 -CS - ES

Typical Codesign Process

System Description(Functional)

HW/SWPartitioning

Software Synthesis

Interface Synthesis

Hardware Synthesis

SystemIntegration

Concurrent processesProgramming languages

Unified representation(Data/control flow)

Instruction set levelHW/SW evaluation

SW HWAnotherHW/SWpartition