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May 10, 2014 R.Innocente 1

Reconfigurable ComputingReconfigurable Computing

Roberto Innocente

[email protected]

mailto:[email protected]


Flexibility

ASICApplication

SpecificIntegrated Circuit

Very inflexible,designed to solve just 1 problem.Energy, space and time

efficient

GPPGeneralPurpose

Processor

Very flexible,can solve any problem. Energy, space and time

inefficient

?

ReconfigurableHardwareFlexible,

But enough energy, time and space efficient

+-


History


Gerald Estrin/1

is credited with the idea, in the '60, of the first reconfigurable

(F+V) FIX+Variable computer

Gerald Estrin. ACM 1960. Organization of computer systems: the fixed plus variable structure computer.


Gerald Estrin/2He envisioned that important gains in performance could be achieved when many computations are executed on appropriate problem oriented configurations.

F+V is made of :

- high speed general computer(the F part) : initially an ibm7090

- various size high speed special structures (the V part) problem specific: trigonometric functions, logarithms, exponential, n-th powers, complex arithmetic, …

V is made of a 36 module positions motherboard which can undergo :

- Function reconfiguration: physically changing some modules

- Routing reconfiguration : changing part of the back wiring

The Rammig machine (1977) : investigation of a reconfigurable machine with no manual or mechanical intervention


Today reconfigurable hardware

Is born out of the will to replace different logic IC(Integrated Circuits), and successively to rapidly prototype large ASICs(Application Specific ICs) or implement SoCs (Sytem On Chip).

In the following slides readers are supposed to be involved in scientific computing and not EE engineers.


Basic digital circuits

AND INVERTER

Shift RegD Type FFMUX

Usually 0=0V, 1=some positive voltage

OR


SSI 74xx IC


PLD

Inconvenience of standard discrete logic circuits :

- 14 pin packages of 4/6 logic functions

- often you had to traverse the PCB to find a free OR or inverter

- if you needed only a few, you had in any case to put an IC with 4/6

Therefore came the idea of PLD (Programmable Logic Device) :

- SPLD (Simple : PAL/PLA)

- CPLD (Complex)

In which a simple interconnection network could be configured melting some internal fuses (fuse technology) to implement combinatorial logic.


disjunctive normal form(aka Sum of products )

Each boolean function of some boolean variables can be represented as a sum of minterms (product of all variables or their complement) .

With 3 boolean vars : a,b,c

are 2 of the 23 = 8 minterms

f (a ,b , c)=a b c+a b c

a b c , a b c


PLA (Programmable Logic Array)

f1=p1+ p2+ p3=x1x2+x1 x3+ x1 x2 x3+x1 x3


FPGA

Also CPLDs showed their limits, therefore in 1985/1990 Xilinx introduced a more flexible design , the

FPGA (Field Programmable Gate Array)

In which the interconnection network is much more flexible and on which also sequential circuits can be easily mapped.


FPGA idea1985 Xilinx – Ross Freeman (inventor of FPGA): “What if we could develop the equivalent of a circuit board full of standard logic parts (like TTL and PAL devices) on a single high density programmable logic chip ?”

- post fabrication programmability by end users

- fabless semiconductor company


Today


FPGA marketDominated by 2 players :

- Altera

- Xilinx

From 67% of 2010, today they share together 90% of the market (4.5 billion usd revenues in 2012)

From sourcetech411(2010)


An important question: are FPGAs green ?

Virtex-7 2000T (one of the top FPGAs) :

~ 20 WXilinx showed 3600 copies of its 8 bit processor nanoblaze running on Virtex-7, consuming 20 W

CPU : ~ 100 WCore i7-4770K Haswell (22 nm) 3.5 GHz@ 4 Cores 84 W

Core i7-3930K Sandybridge-E (32 nm) 3.2 GHz @6Cores 130 W

Xeon E7458 Dunnington (45 nm) 2.4 GHz 90 W

Xeon E7460 Dunnington (45 nm) 2.66 GHz 130 W

GPU : ~ 220 WNvidia Tesla M2090 225 W

Nvidia Tesla K20X 235 W

This is a partial answer. We need to be able to estimate FPGA performance to give a more useful index.


FPGA architecture

From RF and Wireless World

Sea of gates : logic blocks are like islands in a sea of interconnections


Virtex family1998 Virtex 250nm 100mhz 25k-60k cells

2000 Virtex-E 180nm 300mhz 1k-70kcells

2000 Virtex II 150nm to168 mult420mhzupto 93k 4-luts

2005 Virtex-4 90nm 500mhz upto 200k cells

2007 Virtex-5 65nm 550mhz up to 330k cells

Virtex-6 40nm 288-2k DSP to 500k 6-luts

2010 Virtex-7 28nm ~500mhz upto 2000k cells

2014 Virtex-US 20 nm upto 4400k cells

From L Zhuo

Up to ~ 7 billion transistorIntel 2014 15-core Xeon IvyBridge-EX~ 4.3 billion transistorNvidia 2012 GK110 Kepler ~ 7 billion transistor


FPGA/CPU evolution


Virtex-7 is not monolithic

2.5 D technology : 4 FPGA tiles with silicon interposer that provides 10kInterconeections between layers


Enabling technologies


Programming technology/1

Antifuse SRAM

OTP(One time programmable)

Disordered except at very low range

Pass transistor in switch block


Programming technology/2Antifuse

-pros:

cheap, small

-cons:

requires special processing, One time programming

SRAM

-pros:

can be deployed with standard semiconductor process, can be easily reprogrammed

-cons:

large area required(6 transistors)


Confware

The configuration of an FPGA ( that becomes compiled to a stream of bits) is not hardware, nor software.

Someone invented the neologism

confware

The configuration of a reconfigurable hardware.


How you configure an FPGA ?

SRAM cells as a long shift register : loaded serially clocking in the confwareVirtex 7 2000T = 440 Mbits of SRAM cells(simplified : large fpgas can also parallel load the confware)


Logic Blocks/Logic Cells


Fine/coarse grain logic blocksFrom :

- a single transistor (Crosspoint : went in bankrupcy)

- a logic gate

To :

- a complete processor (FPNA: field programmable node arrays)

NB. FPNA is also field programmable neural array


Homogeneous :

- Logic Cells: 4 input LUT(LookUp Table) + FlipFlop

Heterogeneous(modern development) :

- Logic cells

- DSP (Digital Signal Processing)

- Memory blocks

- I/O blocks

The heterogenous architecture is prevalent now. The blocks are configured by SRAM bits usually loaded trough serial ports as already pointed out.

CLB(Configurable Logic Blocks)

Necessary differentiation to allow things like multiplication/addition to be mapped in an efficient way.


Standard Logic Cell

4 input LUT

D type FlipFlop

16 bits of SRAM for conf 1 bit SRAM conf

2:1 Mux


standard LUT (Look Up Table)

0 0000 0

1 0001 1

2 0010 0

3 0011 0

4 0100 1

5 0101 0

6 0110 1

7 0111 1

.. .. ..

Dec Bin Out- 16 x 1 memory

- any boolean function of 4 inputs :

Bit 0

Bit 1

Bit 2

Bit 3

f = x3 x2 x1 x0+ x3 x2 x1 x0+ x3 x2 x1 x0+ x3 x2 x1 x0

NB. LUT rhymes with nut


Uses of Logic Cell

2^4 = 16 x 1 bit memory Any boolean function of 4 inputs

4:1 multiplexer


Virtex-7 Logic Block basics


Virtex-7 Logic sliceFrom Xilinx

4 x 32=128 bit shift reg


Virtex7 CLB slice- 6-input LUT

- 2 5-input LUTs with same inputs

- 2 arbitrary boolean function on 3-input and 2-input or less


Altera ALM


Interconnection network


Interconnection networkHierarchical routing Island type routing(predominant)

Interconnection network can consume 80% of the area of an FPGA !

Nearest neighbours


Programmable switch


SRAM routing: coarse/fine grain5 bit SRAM 1 bit SRAM


Details of island type routing


Disjoint/Wilton switch blocks

Disjoint : wire can only go out on wire of same number, creates routing domainsWilton : can change domain in at least one directions


Channel segments distribution


Columnar architecture7 series Xilinx fpgaColumnar architecture


DSP blocks &floating point


FPGAs floating point in 1994

B. Fagin and C. Renard. Field Programmable Gate Arrays and Floating Point Arithmetic. IEEE Transactions on VLSI Systems, 2(3), September 1994.

Fagin & Renard report that you can implement floating point operators but it is impractical : no

FPGA in existence could contain a single multiplier circuit !!


FPGA fp in 1995

Shirazi & al. On the same line of Fagin & Renard propose 2 custom fp formats 16 and 18 bits total:

they provide for them add,sub, mul, div operators

N. Shirazi, A. Walters, and P. Athanas. Quantitative Analysis of Floating Point Arithmetic on FPGA Based Custom Computing Machines. In Proceedings of the IEEE Symposium on FPGAs for Custom Computing Machines, April 1995.


FPGA fp in 2002Belanovic & Leeser present a library of variable width parameterized floating point operators (superset of the ieee formats)

A Library of Parameterized Floating-point Modules and Their UsePavle Belanovic and Miriam Leeser, 2002


What allowed the breakthrough ?The addition, by major vendors, of hardware multipliers (called DSP blocks) on their FPGA from 2000 on :

- 1st Xilinx on Virtex II

- soon after Altera on Stratix

This started in the last decade also the interest of HPC community :

Cray XD1, Silicon RASC, Convey HC1

HPRC = High Performance Reconfigurable Computing


FPGA MAC operation


Virtex-7 DSP48 high level

From Xilinx

1 bit 2 bit


DSP48E1 details


Altera Stratix V DSP block

4 (*) + 3(+) = 7 flop


Data Flow Graphs (DFG)


Data flow

A representation of a program as a DG(Directed Graph) in which the nodes are the operations and the edges represent the data dependencies from one operation to the next


Control flow/Data Flow

dis2=b**2-4*a*c

If dis2 < 0 complex!

dis=sqrt(dis2)

u1=-b/(2*a)

u2=dis/(2*a)

x1=u1+u2

x2=u1-u2x=

−b2a

±√b2−4ac

2a


A scalar product

Fortran :

acc=0.0

do i=1,4

acc=acc+a(i)*b(i)

enddo

C :

acc=0.0;

for(i=0;i<4;i++){

acc=acc+a[i]*b[i];

}


Time/Space tradeoffs


Systolic array matrix multA(n,n) x B(n,n) requires :2n-1 steps for the last elements to enter the arrayn-1 steps to compute the last c(n,n)n steps to move the result out = 4n-2 steps


Codesign

The implementation of algorithms on FPGAs requires a mix of hw and sw design :

Codesign = hw design + sw design


How to program FPGAs?Mainly with an HDL (Hardware Description Language):

- Verilog(intially developed by Gateway Design Automation, now a std)

- VHDL (out of a standard committee)

But OpenCL, ImpulseC, SystemC, C, Handel-C translators .. are also available.Is this a good idea ?

The problem is that those languages are not thought for describing hardware and the translation finish up usually with a FSM(finite state machine) with 1 state for every statement and then the FSM machine moves along the states .

This is not the way someone skilled would program the FPGA.

Next statelogic

Stateregister

Output Logic

input

clk

D Q

Out

FSM finite state machine


FPGA will win

For many years FPGAs were just prototyping vehicles for ASICs

– Now they are replacing many ASICS & ASSPs

– Watch for the same Trojan effect with FPGAs in HPC


FPGA lingo


Core

Core in FPGA lingo is a function ready to be instantiated into your design as a “black box”. It can be suppliad as HDL or schematic.

It supports design re-use.


Soft/hard cores

On FPGAs functional modules can be implemented :

- using std FPGA resources(logic blocks, DSPs, memory blocks) : softcores

- as an ASIC on the FPGA : hardcores

When the manufacturer puts a processor as an hardcore on the FPGA then it sells this as a SoC (Sytem On Chip) : Dual ARM on Zync-7000 chip, PowerPC on Altera FPGA


IP/open cores

The soft attribute is implied.

Hardware designs in an HDL(eventually using vendor libraries):

- opensource cores : http://opencores.org/

OpenRISC 1000 architecture from the OpenCores community,

the Lattice Semiconductor LM32, the LEON3 from Aeroflex

Gaisler and the OpenSPARC family from Oracle

- proprietary : IP(Intellectual Property) cores

Floating point operators, fft, matrix computations

http://opencores.org/


Commercial offers


Picocomputing

SC6 1U Upto 16 FPGA SC6 4U upto 48

EX-600EX-800

FromPICOCOMPUTING


Bittware Terabox

16 altera stratix-V

From Bittware


DINIGROUP Cluster of 4 Virtex7

From DINIGROUP


Dinigroup Cluster 40 Kintex-7

From DINIGROUP


Maxeler MPC-X

Daresbury Lab UK :The dataflow supercomputer will feature Maxeler developed MPC-X nodes capable of an equivalent 8.52TFLOPs per 1U and 8.97 GFLOPs/Watt.


Convey HC-2 , HC-2ex


Cray XT5h

“Cray introduces an hybrid supercomputer thatcan integrate multiple processor architectures into a single system and accelerate high performance computing (HPC) workflows. The Cray XT5h delivers higher sustained performance, by applying alternative processor architectures across selected applications within an HPC workflow. The Cray XT5h supports avariety of processor technologies, including scalar processors based on AMD OpteronTM dual and quad-core technologies, vectorprocessors, and FPGA accelerators.”


CHRECCenter for High PerformanceReconfigurable ComputingUF/BYU/GWU/VTECH


CHREC Novo-G 384 FPGAs“Novo-G is the most powerful reconfigurable supercomputer in the known world. This unique machine features 192 top-end, 40nm FPGAs (Altera Stratix-IV E530) and 192 top-end, 65nm FPGAs (Stratix-III E260). “

http://www.chrec.org/

(pronounce it as shreck)

http://www.chrec.org/


BLAST like Smith-Waterman computes local alignment of 2 sequences :

- Novo-BLAST Novo-G/CHREC implementation : faster, same sensitivity

IPC(Isotope Pattern Calculator) of Protein Identification Algorithm :

- speed up 52-366 on single fpga, 1259 on 4 fpgas, 3340 on a node(16 fpgas)

CHREC/2


References forApplications


Linear Algebra for RC

Juan Gonzalez and Rafael C. NúñezLAPACKrc: Fast linear algebra kernels/solvers for FPGAaccelerators(JP 2009)DOD funded


DCT, FFT on FPGAs

Digital Signal Processing with Field Programmable Gate Arrays ,3d edition(2007)

U.Mayer Baese, Springer Verlag


MD on FPGA There are many papers about porting Molecular Dynamics algorithms on FPGAs with substantial positive conclusions about experiments on 1-2 FPGAs. But in the last years there is an embarassing comparison with ANTON (Shaw et al.).

We cant forget that ANTON is a really huge machine consuming over 100 KW !!!!

And is made out of 512 dedicated ASICs at 1ghz!

The comparison with some FPGAs consuming 40/60 W is improper.

FPGA-Accelerated Molecular Dynamics(2013) M. A. Khan,M. Chiu, M. C. Herbordt


Neural networks on FPGAs

Editors : Omondi , Rajakapse (2006)

FPGA implementation of neural networks

ANN(Artificial Neural Network) in integer arithmetic performs 40x better than on GPP (old FPGA, 3 generation old)


Altera Arria 10


Arria10


Arria 10 variable precision DSP block

Altera

A

B

CD

A+C*D = 2 flop


Arria10 estimated sp fp performance

- 2 flops per cycle

- 1688 fp single precision DSP (GX660)

1688*2 = 3376 flops per cycle

3376 * 0.5 ghz ~ 1.7 Teraflops in single precision


Hard single prec FP on FPGA ?!?

For people that can live with single precision this seems a very attractive new feature.

But many think that it is too much a waste of generic resources and claim that what was missing were simpler blocks !


Back of the envelopeperformance estimation


Back of the envelope performance estimation

Given number of

- LUTs

- FFs

- DSPs

offered by an FPGA,

and utilization of resources by operators, estimate the max number of operators that can be implemented on the FPGA

Today FPGA clocks are ~500Mhz=0.5GHz(unavoidable price for flexibility)2000 flops per cycle = 1 Teraflops


Xilinx Virtex-7 family

Virtex-7 slices : 4 x 6-input LUTs, 8 FFsVirtex-7 DSPs : 48 bits pre-adder, 25x18 multiplier, 48 bits accumulatorVirtex LUT ~ 1.6 standard LUT


Custom precision 17/24 bits floating

dsp lut+f lut f # tot dsp tot lut tot f* 2 103 90 112 1080 2160 208440 232200

1 113 97 104 0 0 0 00 377 336 376 0 0 00 0 0 0 0 0 0

0 0 0+ 0 369 301 393 1510 0 1011700 1150620

0 0 0 0 0 0 0 0

Tot 2590 2160 1220140 1382820

Virtex-7 V2000T available resources

slices LUT x FF x dsp 6 input ff

slice slice LUT305400 4 8 2160 1221600 2443200

1.6standard LUTs 1954560


IEEE single precision – 32 bits


2 160 128 160 0 0 0 01 331 283 331 0 0 00 665 629 669 0 0 0

0 0 0+ 2 293 225 327 25 50 12950 15500

0 500 407 541 1160 0 1052120 1207560

Tot 1885 2150 1221170 1380560



slice slice LUT305400 4 8 2160 1221600 2443200



IEEE double precision – 64 bits


10 371 299 456 0 0 0 09 439 356 510 0 0 00 2361 2317 2418 0 0 0

0 0 0+ 3 895 705 945 1 3 1600 1840

0 989 794 1029 617 0 1100111 1245106

Tot 814 2159 1220095 1393162



slice slice LUT305400 4 8 2160 1221600 2443200



Virtex UltraScale XCVU440 20nm -sampling outIEEE double precision – 64 bits


10 371 299 456 0 0 0 09 439 356 510 0 0 00 2361 2317 2418 0 0 0

0 0 0+ 3 895 705 945 3 9 4800 5520

0 989 794 1029 1321 0 2355343 2665778

Tot 1585 2880 2517787 2866004

Virtex Ultra Scale - available resources


slice slice LUT314820 8 16 2880 2518560 5037120



Relative power dissipation/1TDP/peak nominal double fp performance :

Intel Q6600 2.4ghz 105W/ 38 gflops = 2763mW/gflops

Intel Haswell i7-4770K 3.5ghz 84W/ 112 gflops = 750mW/gflops

Intel IvyBridge 3770K 3.5ghz 77W/ 112 gflops = 687mW/gflops

Nvidia Tesla M2090 225W/ 666 gflops = 337mW/gflops

Nvidia Tesla K20X 235W/1310gflops = 179mW/gflops

Xilinx Virtex-US 20W/ 800gflops = 25mW/gflops Ro w 1 Ro w 2 Ro w 3 R ow 4

0 C ol um n 1C ol um n 2C ol um n 3

FPGA computing = green computing

}} ~10x

~30x


Relative power dissipation/2

Intel 2.4 ghz q6600

intel 4770k

intel i7-3770k

tesla m2090

tesla k20x

virtex7

0 500 1000 1500 2000 2500 3000

mW / Gflops

mW


Gflops per Wattpeak nominal double fp performance/TDP :

Intel Q6600 2.4ghz 38 gflops/105 W = 0.36 gflops/W

Intel Haswell i7-4770K 3.5ghz 112 gflops/84 W = 1.33 gflops/W

Intel IvyBridge 3770K 3.5ghz 112 gflops/77 W = 1.45 gflops/W

Nvidia Tesla M2090 666 gflops/225 W = 2.96 gflops/W

Nvidia Tesla K20X 1310 gflops/235 W = 5.57 gflops/W

Xilinx Virtex-US 800 gflops/20 W = 40 gflops/W Ro w 1 Ro w 2 Ro w 3 R ow 4

0 C ol um n 1C ol um n 2C ol um n 3

FPGA computing = green computing

}} ~10x

~30x


Top green500 listgreen500_ranktotal_power Year name Total CoresName ManufacturerCountry

1 28 4,503 2013 2720 TSUBAME-KFC NEC Japan2 53 3,632 2013 5120 Wilkes Dell United Kingdom3 79 3,518 2013 4864 HA-PACS TCA Cray Inc. Japan4 1,754 3,186 2012 115984 Cray Inc. Switzerland5 81 3,131 2013 5720 romeo Bull SA France6 923 3,069 2013 74358 TSUBAME 2.5 NEC/HP Japan7 54 2,702 2013 3080 IBM United States8 270 2,629 2013 15840 IBM Germany9 56 2,629 2013 3264 IBM United States

10 71 2,359 2010 4620 CSIRO GPU Cluster Xenon SystemsAustralia11 179 2,351 2012 38400 SANAM Saudi Arabia12 82 2,299 2011 16384 IBM United States13 82 2,299 2012 16384 Cetus IBM United States14 82 2,299 2012 16384 IBM Poland15 82 2,299 2013 16384 IBM United States16 82 2,299 2012 16384 Vesta IBM United States17 82 2,299 2012 16384 IBM United States18 237 2,243 2013 10920 HPCC Hewlett-PackardUnited States

Mflops/WattLX 1U-4GPU/104Re-1G Cluster, Intel Xeon E5-2620v2 6C 2.100GHz, Infiniband FDR, NVIDIA K20xDell T620 Cluster, Intel Xeon E5-2630v2 6C 2.600GHz, Infiniband FDR, NVIDIA K20Cray 3623G4-SM Cluster, Intel Xeon E5-2680v2 10C 2.800GHz, Infiniband QDR, NVIDIA K20xCray XC30, Xeon E5-2670 8C 2.600GHz, Aries interconnect , NVIDIA K20xPiz DaintBull R421-E3 Cluster, Intel Xeon E5-2650v2 8C 2.600GHz, Infiniband FDR, NVIDIA K20xCluster Platform SL390s G7, Xeon X5670 6C 2.930GHz, Infiniband QDR, NVIDIA K20xiDataPlex DX360M4, Intel Xeon E5-2650v2 8C 2.600GHz, Infiniband FDR14, NVIDIA K20xiDataPlex DX360M4, Intel Xeon E5-2680v2 10C 2.800GHz, Infiniband, NVIDIA K20xiDataPlex DX360M4, Intel Xeon E5-2680v2 10C 2.800GHz, Infiniband, NVIDIA K20xNitro G16 3GPU, Xeon E5-2650 8C 2.000GHz, Infiniband FDR, Nvidia K20mAdtech, ASUS ESC4000/FDR G2, Xeon E5-2650 8C 2.000GHz, Infiniband FDR, AMD FirePro S10000AdtechBlueGene/Q, Power BQC 16C 1.60 GHz, CustomBlueGene/Q, Power BQC 16C 1.600GHz, Custom InterconnectBlueGene/Q, Power BQC 16C 1.600GHz, Custom InterconnectBlueGene/Q, Power BQC 16C 1.600GHz, Custom InterconnectBlueGene/Q, Power BQC 16C 1.60GHz, CustomBlueGene/Q, Power BQC 16C 1.60GHz, CustomCluster Platform SL250s Gen8, Xeon E5-2665 8C 2.400GHz, Infiniband FDR, Nvidia K20m


Power/Energy efficiency


Power Dissipation

PT=k C V 2 f +Ps

Ed=12

C V 2

A chip is made of millions of CMOS FETs. When input switches, you need to charge the small capacitance :

f times a second gives, together with some constant static dissipation :

Anyway increasing a lot the frequency, the chip becomes unstable unless you increase also the voltage(leakage). Therefore there is in fact a superlinear behaviour vs f:


Dennard scaling(1974)

1

S

S3

S2 = 2x moretransistors

S = 1.4x lowercapacitance

Scale Vdd by S => S2 = 2x lower energy

S2S = 1.4x fastertransistors

Performance scales as S3 = 2.8 while power density stays constant across generations


Fred Pollack(Intel) famous graph(1999)

Power density increases !!!In 2004/2005 we hit the power wall => stop frequency increases

“New microarchitecture challenges in the coming generations of CMOS process technology” F.Pollack


End of Dennard scaling

1

S

S3


S = 1.4x lowercapacitance S2

S = 1.4x fastertransistors

In submicron technology rigidity in voltage scaling. Power increases by S2 = 2


MOS subthreshold currentScaling down geometry you scale down drain voltage to avoid high electric fields and to decrease energy required to switch. You have to scale down also the threshold voltage to sustain the 30% decrease of gate delay. The small voltage swing that remains is not able to completely turn off the transistor. Subthreshold leakage that was ignored in the past can on modern VLSI chips consume up to ½ of the total power.


Subthreshold leakage


VT

design tradeoff

VGS

log IDS

- Low VT for high ON current :

- High VT for low OFF current

Phenomenology :60-200 mV of V

GS swing decreases I

DS by

one order of magnitude. Today 0.5-0.2V

T doesn't allow the needed swing of V

GS to

shutoff the transistor.

I Dsat ∝(V DD−V T )2

Low VT

=> high IDS

good for ON condition

High VT => low leakage

good for OFF condition


Multicore scaling

65 nm 45 nm 32 nm

4-core 8-core 16-core

Every generation 2x cores, at same or slightly increasing frequency.


Multicore scaling at constant frequency

1

SS2


S = 1.4x lowercapacitance

} S = 1.4x lowerutilization

We hit the utilization wall => dark silicon


End of multicore scaling

65 nm 32 nm

4 cores 8 cores

Every generation 1.4x cores, at same or slightly increasing frequency.

Dark or dim silicon(“uncore”)

45 nm

5.7 cores


Dark silicon and the end of multicore scaling

Doug Burger (Microsoft) at HiPEAC 2013 :

- till 2004: each semiconductor generation gave transistors smaller, faster and that consume less

- from 2004 to now: we still got smaller transistors, but we could not run them faster (power wall)

- in the future : we will still get smaller transistors but we will not be able to use all of them together(dark silicon) or at max speed.


Scaling the utilization wallG.Venkatesh ASPLOS 10 :

“while the area budget continues to increase exponentially, the power budget has become a first-order design constraint in current processors. In this regime, utilizing transistors to design specialized cores that optimize energy-per-computation becomes an effective approach to improve the system performance.

”The Utilization Wall : With each successive process generation, the percentage of a chip that can switch at full frequency drops exponentially due to power constraints. [Venkatesh, ASPLOS ‘10]

Single chip heterogeneous computer (E.Chung)

Greater energy efficiency combining GPP with unconventional cores (U-cores) : GPU,FPGA,DSP,ASICs ..


3D FinFET promiseBelow 20nm the roadmap is to use 3D FinFETs :- Faster : +37%- Dynamic Power: -50%- Static Power: -90%

KAIST demonstrated a 3nmFinFET in lab


The trouble with multicoreA famous article of David Patterson (of “Computer architecture: a quantitative approach” fame) on IEEE Spectrum, 2010 :

“Chipmakers are busy designing microprocessors that most programmers can’t program”

“... the semiconductor industry threw the equivalent of a Hail Mary pass when it switched from making microprocessors run faster to putting more of them on a chip - doing so without any clear notion of how such devices would in general be programmed. The hope is that someone will be able to figure out how to do that, but at the moment, the ball is still in the air.”


Verilog


Using Verilog

You write a functional specification (usually) splitted in modules that documents the exact behaviour of the system.

LogicSynthesis

Place &Route

HDL (Verilog)

FPGAASIC

Functionaldesign

Physicaldesign

Gatenetlist

Simulated annealing used here !

NB. place and route of a large design can take 1 day of a fast CPU !!


Verilog/1

Basic module :

// comments in this waymodule name(input x0,x1,input [3:0]y, output out);// x0,x1 are wires, y is a 4 wires bus// out is an output wire// combinational logic use assign wire x0,x1, [3:0]y, outendmodule


Verilog/2

Combinatorial circuit :// performs not a b c + a not b not cmodule dummy(input a,b,c, output y,z); wire a,b,c,y; assign y = ~a & b & c | a & ~b & ~c; assign z = ~c;endmodule

This is not C ! a,b,c,y,z are wires and y,z change whenever

a or b or c change. To avoid this drama for complex circuitswe use synchronous logic

(everything is stepped in docking stations = Flip flops)


Verilog/3


Verilog/4

A sequential circuit :// a flip flop described in verilogmodule ff(input d, clk, output q, qbar); wire d, clk; reg q, qbar; always @(posedge clk) begin q <= d; qbar <= ~d; endendmodule

At a raising edge of the wire clk copy the signal to q and

the inverse of d to qbar


Verilog/5


Verilog/6A more complicate sequential circuit :

// in verilog FF with clear/resetmodule ff(input d, clk,clr, output q, qbar); wire d, clk; reg q, qbar; always @(posedge clk, posedge clr) if (clr) q <= 0; else begin q <= d; endendmodule

At a raising edge of the wire clr set q=0, at the raising edge

of clk copy the signal to q and the inverse of d to qbar


Verilog/7


BORPH : Berkeley Operating system for ReProgrammable HardwarePETALINUX : Xilinx linux for Zynq et al.


- Idea of HW unix process : has pid, can be killed like a normal unix process, but in fact is an HW instance on FPGA

- ioreg Virtual File System interface

Borph : Berkeley Operating System


Xilinx Petalinux

The PetaLinux Software Development Kit (SDK) is a development tool that contains everything necessary to build, develop, test and deploy Embedded Linux systems on : Zync-7000, Zedboard, Kintex-7 boards.

PetaLinux consists of : pre-configured binary bootable images, fully customizable Linux for the Xilinx device, and PetaLinux SDK which includes tools and utilities to automate complex tasks across configuration, build, and deployment.

PetaLinux is offered under two separate licenses :

No charge Evaluation license or Commercial licenses


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