Mixed-Signal Circuits for the Data-Driven World

Boris Murmann

September 8, 2015

Research Overv iew

Murmann Mixed-Signal Group

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Student Support (Past and Present)

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Chip Foundry Support

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The Big Picture

Various terminology for the same overarching trend

› Third paradigm

› Ubiquitous computing

› Internet of Everything

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Hardware Platforms Physical World

Software, Networks Virtual World

Fusion of Physical and Virtual Worlds

The Internet of Everything

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Source: Vivante Corporation

This Trend is Real and Here to Stay…

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Source: Kim, ISSCC 2015, Keynote Talk

Needs in Mixed-Signal IC Design

All-time classics

› Low-power analog interfaces

› Low-power wireless and wireline links

› Efficient power management and energy harvesting

Frontiers

› Improved interfacing with the biological world

› In-sensor computing, machine learning

› Flexible and stretchable electronics

Overarching

› Boost in design productivity

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Current Projects (1)

ADCs and wireline communication circuits

› High-resolution SAR ADC with loop-embedded buffer

› IF ADC for digital radar

› Wideband ADC for optical communications

› Analog equalization in ADC-based serial links

› Flash ADC for serial links

RF building blocks

› Compressed sensing RF receiver

› Low-rate training of PA pre-distorters

Power management

› Energy harvesting for CMOS imagers

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Current Projects (2)

Instrumentation and sensing

› Finite rate of innovation ultrasound interface

› Always on image sensor

› Densely integrated 3-D ultrasound interface

› Label free biomolecule detection

› Analog interfaces using stretchable organic TFTs

Custom circuits for machine learning

› Architecture optimization of deep neural networks

› Hardware implementation of deep neural networks

Design productivity

› Automatic layout generation for analog FinFet circuits

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Drive Problem in High-Resolution SAR ADC

Tracking time is only a small

faction of the cycle

Large input capacitance

(several picofarads)

Driver often consumes more

power than SAR ADC

Reference: Kramer, ISSCC 2015

V

Ttracking Tconversion

t

SAR

ADCBufferSignal Dout

Cs

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Conceptual Block Diagram

LogicVin

DAC

VDAC

Vres

DAC

BufferT&H

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Moving the Buffer Inside the Loop

Buffer is linearized by the loop gain of the SAR algorithm

Can use a power efficient, weakly nonlinear source follower

DAC

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Implementation (14b, 35MS/s)

Buffer nonlinearities must be “static” to prevent path mismatch

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Reference: Kramer, ISSCC 2015

200fF

12.5mW

30mW

Simulated Source-Follower Nonlinearity

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106

107

108

-100

-80

-60

-40

-20

0

f [Hz]

dB

FS

Fundamental

HD2

HD3

HD4

HD5

17.5 MHzfs/2

[dB

]

ComparisonThis Work Kapusta, ISSCC’13 Inerfield, VLSI’14

Architecture SAR TI-SAR SAR

CMOS technology (nm) 40 65 28

Resolution (bits) 14 14 15

Sampling rate (MS/s) 35 80 100

SNDR at low freq. (dB) 75 73.6 71

SNDR at Nyq. (dB) 74.4 71.3 67.1

SFDR at low freq. (dB) 99 85 89.2

SFDR at Nyq. (dB) 90 80 76

Integrated buffer Yes No Yes

Power incl. reference (mW) 42.5 35.1 8.0

Power of input buffer (mW) 12 - Not reported

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Power Distribution (54.5mW total)

Future work should replace the current steering DAC with an SC DAC

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Old School Signal Processing Pipeline

Severe bottlenecks going forward

› Energy of brute-force digitization

› Energy of sending bits to/through the cloud

A/D mP Cloud

Information

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Research Opportunities

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Analog-to-

Information

Classification/

InferenceCloud

Actionable output

Generate only as many bits as needed to capture relevant information

Exploit the robustness of classification/inference algorithms to get by with

approximate computing and/or memory

Precursor of a New Direction – Motion Processor

Do not feed raw inertial sensor data to main processor

Fuse sensors and output features (detected gestures, etc.)

Invensense MotionProcessing™ platform, www.invensense.com

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Example: “Always On” Vision Sensor

[Choi, ISSCC 2015]

Standard imaging pipeline optimized for

visually appealing images

How to approach fundamental power

limits for pure classification tasks?

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Logarithmic Frontend for HOG Features

Acquisition of log

gradients with 2-3 bit

resolution is sufficient

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Example: Ultrasound Imaging

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[Analog Devices]

20 frames/s

256 Channels

X 50 MS/s

X 12 bits

= 154 Gb/s (!)

Transducer Signal Spectrum

0 1 2 3 4 5 6 7-70

-60

-50

-40

-30

-20

-10

0

f [MHz]

Norm

aliz

ed S

pect

rum

[dB

]

Nyquist

fs > 2·7.5 MHz

With margin

for filter roll-off

fs ~ 30…50

MHz

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Time Domain Signal

0 20 40 60 80 100 120-1

-0.5

0

0.5

1

Time [ms]

Tran

sduc

er S

igna

l [a.

u.]

Depth

Scan

Line

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Time Domain Signal

0 20 40 60 80 100 120-1

-0.5

0

0.5

1

Time [ms]

Tran

sduc

er S

igna

l [a.

u.]

Depth

Scan

Line

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Time Domain Signal (Zoom)

Reflections carry the transducer’s pulse response signature

There are really just two pieces of information in each reflection:

Arrival time and amplitude

79 80 81 82 83-0.2

-0.1

0

0.1

0.2

Time [ms]

Tra

nsdu

cer

Sig

nal [

a.u.

]

105 106 107 108

-0.1

-0.05

0

0.05

0.1

Time [ms]

Tra

nsdu

cer

Sig

nal [

a.u.

]

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Time Domain Signal (Zoom)

Yet, we sample at Nyquist and accurately digitize the (known) pulse

response of the transducer over, and over, and over again…

79 80 81 82 83-0.2

-0.1

0

0.1

0.2

Time [ms]

Tra

nsdu

cer

Sig

nal [

a.u.

]

105 106 107 108

-0.1

-0.05

0

0.05

0.1

Time [ms]

Tra

nsdu

cer

Sig

nal [

a.u.

]

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Innovation Rate Sampling

Input After Filter

Sample ~50x

below Nyquist

Conventional Innovation Rate

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Example: Digital Predistorter Training

Acquire wideband spectrum and digitize GS/s just to estimate/track

50-100 parameters?

PAz[n]

y[n]Modelextraction

DPDx[n]

ADC

DAC

y(t)

x(t)

𝑒𝑖Ω𝑐𝑡𝑒−𝑖Ω𝑐𝑡

fs ~ 1.4 GS/s

BW ~ 700 MHz

Resolution ~12 bits

100 MHz

700 MHz

10-50 parameters

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Sampling the Spectrum, not the Time-Domain Signal

Obtain spectrum samples according to “degrees of freedom”

Independent of signal bandwidth, Nyquist rate

f

Downconversion

0

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Implementation

N. Hammler, Y.C. Eldar, and B. Murmann, “Low-Rate Identification of

Memory Polynomials, ISCAS 2014.

Windowed

integration

Slow ADCs

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Identification of an LDMOS Doherty PA

10MHz WCDMA input

1024-fold fs reduction

(92.16 MHz to 90 kHz)

(Measured result)

N. Hammler, Y.C. Eldar, and B. Murmann, “Low-Rate Identification of

Memory Polynomials, ISCAS 2014.

102

103

104

105

-40

-38

-36

-34

-32

-30

-28

-26

-24

-22

X: 4.608e+004

Y: -39.48

# measurements M

X: 95

Y: -38.51

NM

SE

[dB

]

conventional method

proposed method

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Compressed Sensing RF Receiver

𝑦[𝑛]𝑥(𝑡)

𝑝(𝑡)(𝑇𝑝 Periodic)

Binary sequence

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𝑌1(𝑒𝑗𝜔)

𝑌2(𝑒𝑗𝜔)

𝑌3(𝑒𝑗𝜔)

=

𝑃𝑎 𝑃𝑏 𝑃𝑐𝑃𝑎′ 𝑃𝑏

′ 𝑃𝑐′

𝑃𝑎′′ 𝑃𝑏

′′ 𝑃𝑐′′

𝑋(𝑗𝜔𝑎)𝑋(𝑗𝜔𝑏)𝑋(𝑗𝜔𝑐)

𝑦1[𝑛]

𝑦2[𝑛]

𝑦3[𝑛]𝑥(𝑡)

𝑝(𝑡)

𝑝′(𝑡)

𝑝′′(𝑡)

Yonina Eldar’s Modulated Wideband Converter

[Mishali, Eldar, “From Theory to Practice…”, 2010]

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Spectrum Sensing Using Frequency-Diverse Sequence

……𝑋(𝑗𝜔)

……𝑃(𝑗𝜔)

𝑋 𝑗𝜔 ∗ 𝑃(𝑗𝜔)

Signal

Mixer

BB

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Spectrum Sensing Using Frequency-Diverse Sequence

……𝑋(𝑗𝜔)

……𝑃(𝑗𝜔)

𝑋 𝑗𝜔 ∗ 𝑃(𝑗𝜔)

Signal

Mixer

BB

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Spectrum Sensing Using Frequency-Diverse Sequence

……𝑋(𝑗𝜔)

……𝑃(𝑗𝜔)

𝑋 𝑗𝜔 ∗ 𝑃(𝑗𝜔)

Signal

Mixer

BB

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Optimized Sequence for Reception

……𝑋(𝑗𝜔)

……𝑃(𝑗𝜔)

𝑋 𝑗𝜔 ∗ 𝑃(𝑗𝜔)

Signal

Mixer

BB

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Hardware Based Harmonic Cancellation

Sequence design offers limited blocker rejection

The 𝑛𝑡ℎ harmonic of a periodic signal delayed by 𝜏 is phase offset by 𝑛𝜔𝜏

› Choose 𝜏 such that 𝑛𝜔𝜏 = 𝑘𝑜𝑑𝑑𝜋

The 𝑛𝑡ℎ harmonic of 𝑝 𝑡 + 𝜏 is then 180° out of phase with that of 𝑝 𝑡

› Sum has no 𝑛𝑡ℎ harmonic

This is a generalization of a technique used in power inverters*

*[Kassakian, Schlecht, Verghese. “Principles …” 1991]

𝑝(𝑡)

𝑝(𝑡 + 𝜏)

𝑐𝑛

𝑐𝑛𝑒𝑗𝑛𝜔𝜏

𝑐𝑛 + 𝑐𝑛𝑒𝑗𝜔𝜏

180°

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Block Diagram of Prototype IC

Five identical branches

› Four active branches

› A rotating calibration branch

A test tone is used to calibrate

the mixing coefficients and the

harmonic cancellation delay

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Design Specifications Target

Target frequency band 699-915 MHz

Channel bandwidth 1.5 MHz

Sequence-based

harmonic rejection

30-40 dB

Harmonic cancellation-

based rejection

30-40 dB

Digital to Delay Converter (t Block)

Coarse adjustment by shifting the data in the shift register

› Adjustment range: 0 − 667 𝑛𝑠, increments of 500 𝑝𝑠

Fine adjustment by switching in or out extra capacitance

› Adjustment range: 0 − 594 𝑝𝑠, increments of 2 𝑝𝑠

500 𝑝𝑠 Resolution 2 𝑝𝑠 Resolution

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Compute Fabric for Convolutional Neural Networks

Lane tracking and similar applications burn ~100 W on a GPU board

A. Coates, 2014

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State-of-the Art Custom Hardware

Main idea: Bring memory closer to computation

Read from 256-bit, 36MB eDRAM: ~19 pJ (off-chip DRAM is ~6 nJ)

Advancements in 3D integration will improve this further

And some point we should become compute-limited

› Opportunity for analog computing?

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Reference: Y. Chen, MICRO 2014

Area: 67.7 mm2

Digital Multiplier

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Charge Domain Dot Product Kernel

~5x energy reduction over

custom digital

SIGN

VDD/2

SIGN

8Cu 4Cu 2Cu Cu

8Cu 4Cu 2Cu Cu

+

−

vOD

QP

QN

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Example: 12 x 9

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VREF

RESET

INW

SHARE

INX

SHARE2Cu8Cu 4Cu Cu

2Cu8Cu 4Cu Cu

2Cu8Cu 4Cu Cu

2Cu8Cu 4Cu Cu

2Cu8Cu 4Cu Cu

𝑄𝑊 = 12𝐶𝑢𝑉𝑟𝑒𝑓

𝑉𝑊 =12

15𝑉𝑟𝑒𝑓

𝑄𝑊𝑋 =12

15𝑉𝑟𝑒𝑓 ⋅ 9𝐶𝑢

𝑉𝑊𝑋 =12

15⋅9

15𝑉𝑟𝑒𝑓

(Logical inverse of 9)

Charge Domain Dot Product Energy Breakdown

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The Machine Learning Team: Top-Down Approach

DX,1 DY,1

VX∙YADC

DOUT,X∙Y

DX,2 DY,2 DX,N-1 DY,N-1 DX,N DY,N

ALU

ALU

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Architecture

Digital, memory

Analog

Summary

The transition to the “third paradigm” brings interesting

opportunities and challenges

› Mixed-signal IC design is no exception

Improving mixed-signal components is still important

› But we must increasingly follow a system/application-guided

approach for large returns

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