0 0.2 0.4 0.6 0.8 1 1.2

VDD (V)

0.001

0.01

0.1

1

En

erg

y (

no

rm.)

0.3V

12x

  

Antenna

Voltage Doubler

Load (LED) Impedance Matcher

  

NEED TO REVISIT WHAT WE

MEAN BY COMPUTATION

2-3 orders more efficient than today’s silicon equivalent (>1016 FLOPS with ~20 W)

Robustness in presence of component failure and variations

  Neural response is highly variable (σ/μ≈1) [Faisal]

Amazing performance with mediocre components

  E.g. sensory pathways– auditory, olfactory, vision,

Physical Interface Platforms across Scale and Modality

μECoG+BMIPeter Ledochowitsch / Aaron Koralek

Carmena / Maharbiz

64 channel remotely powered wireless uECoG [Muller, Le, Ledoschowicz, Li]

Free-floating wireless AP acquisition electrodes [Biederman, Yeager, VLSI12]

Register Bank

Memory Feature Extraction

Preamble Buffer

Spike Detection

Spike Alignment

Asynchronous 250 nW/channel spike-sorting [Liu, VLSI12]

μ

[DJ Seo et al, Arxiv, June 2013]

  

200 400 600 800 1000 1200 1400 1600 1800 2000

-40

-20

0

20

i d

Vol

tage

(mV

)

Input

50 100 150 200 250 300

-40

-20

0

20

Slow conducting peaks

200 400 600 800 1000 1200 1400 1600 1800 2000

-40

-20

0

20

Vol

tage

(mV

)

InputReconstructed

• – – 

– 

• 

– 

– – 

Functional non-determinism present in most applications related to human-cyber interfaces

feature extraction, classification, synthesis, recognition, decision making, learning,

Choose the right information representation that   makes computation easy, efficient and robust   matches the platform!

CNFET+RRAM Sparse Vector Generator

Correlation Matrix Memory

Energy-efficient Analog Adaptive

Front End

Rec

ogni

tion

Sen

sor R

espo

nses

Sparse Representation

Storage Recognition Feature Extraction

• 

• 

‘WEAK’ Linear Classifier 2

Sensor Data

Trainer

‘WEAK’ Linear Classifier 1

Weight 1

Weight 2‘STRONG’

ClassificationResult

Weighted Voter

Training Data

Model

Weak classifier fault due to circuit

non-idealities

DAQ SYSTEM

/ PC

SENSOR ARRAY THIN-FILM CLASSIFIERProjector

8x8cm photoconductor plane on glass

1cm

MB-GND

MS-

MB+

MS+

Weak TFT classifier branches on glass

Probe card for testing

Rat

es (t

p &

tn)

1 2 3 4 5 60.30.40.50.60.70.80.9

1

tntp

No. of weak classifiers

(measured)

tnSVM tp

2-5 weak classifiers based on highly non-ideal (TFT) multipliers achieve performance of ideal SVM

Kiji

j

 

 

∂θi∂t =ωi + Kij

j=1

M

∑ sin(θ j −θi )

• • 

   

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

10

20

30

40

50

60

70

80

 

 

 

 

Learning

(Correlation)

Retrieval (Projection)

(Thresholding)

   

ILV RRAM

CNFET

106

1080

5

RRAM ( )

RR

AM

/

Target / > 1

Measured CNFET σ/μ = 0.57

Delay Cell Coincidence Detector

100 μm

Delay Cell

RRAM

CNFETs

ILVs

100 μm

 

 

0

0.5

1

1.5

2

2.5

3

3.5

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

Dis

trib

utio

n

VX (V)

Up3Dn2

Up3Dn1

Up1Dn1

ZN=1 ZN=0

w/o IOFF with IOFF w/o IOFF with IOFF w/o IOFF with IOFF

 

-  

-  

 

THANK YOU!

MERCI BEAUCOUP!