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The [http://iip.ethz.ch Integrated Information Processing (IIP) Group] carries out research in the following areas:
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Image:iip_presentation_jan23.jpg|800px
rect  15   530 630 15 [[Theory, Algorithms, and Hardware for Beyond 5G]]
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rect  1   1 852 740 [[Theory, Algorithms, and Hardware for Beyond 5G]]
rect  700   530 1315 15 [[Positioning with Wireless Signals]]
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rect  953   1 1802 740 [[Positioning with Wireless Signals]]
rect  1400   530 2010 15 [[Simultaneous Sensing and Communication]]
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rect  2100   530 2720 15 [[All-Digital In-Memory Processing]]
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rect  15   1100 630 585 [[Analog-to-Information Conversion for Low-Power Sensing]]
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rect  1   800 852 1540 [[Analog-to-Information Conversion for Low-Power Sensing]]
rect  705   1100 1315 585 [[Nonlinear Digital Signal Processing]]
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rect  953   800 1802 1540 [[Mixed-Signal Circuit Design]]
rect  1400   1100 2010 585 [[Real-Time Optimization]]
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rect  1903   800 2751 1540 [[Real-Time Optimization]]
rect  2100   1100 2720 585 [[Audio_Signal_Processing]]
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rect  2853   800 3702 1540 [[Audio Signal Processing]]
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= Integrated Information Processing Group =
 
The Integrated Information Processing (IIP) Group carries out research in the following areas:
 
  
 
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====
 
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====
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Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.
 
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.
  
====[[Nonlinear Digital Signal Processing]]====
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====[[Mixed-Signal Circuit Design]]====
Nonlinearities play a critical role in a large number of signal processing applications, including
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All-digital beamforming architectures for massive multi-antenna (MIMO) wireless systems provide best-in-class beamsteering capabilities and simplify many baseband processing tasks. The drawback of such architectures is the need for a large number of radio-frequency (RF) frontends (FEs), which can result in high power consumption and large silicon area. The projects in this area focus on jointly designing and optimizing mixed-signal RF circuits and digital baseband processing implementations to avoid the drawbacks of all-digital beamformers.
the areas of wireless communication, image processing, and machine learning. Unfortunately, analyzing
 
the fundamental properties of nonlinear systems and estimating signals from nonlinear measurements are
 
notoriously difficult tasks. The projects in this area focus on analyzing nonlinear systems and developing new algorithms that compensate nonlinear behavior or estimate quantities from nonlinear observation models.
 
  
 
====[[Real-Time Optimization]]====
 
====[[Real-Time Optimization]]====
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====[[Audio Signal Processing]]====
 
====[[Audio Signal Processing]]====
Machine learning and deep neural networks are currently revolutionizing a variety of applications, including the well-established field of digital signal processing. The projects in this area focus on the design of novel algorithms that enable real-time audio signal processing using emerging tools from machine learning and their implementation on digital signal processors (DSPs) or hardware accelerators (FPGAs and ASICs).
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Machine learning and deep neural networks are currently revolutionizing a variety of applications, including the well-established field of digital signal processing. The projects in this area focus on the design of novel algorithms that enable real-time audio signal processing using emerging tools from digital signal processing and machine learning. The ultimate goal is their realization on digital signal processors (DSPs) or hardware accelerators (FPGAs and ASICs).
  
 
=Available Projects=
 
=Available Projects=
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===[[Nonlinear DSP]]===
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===[[Mixed-Signal Circuit Design]]===
 
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category = Available
 
category = Available
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category = Available
 
category = Available
 
category = IIP_AUDIO
 
category = IIP_AUDIO
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==Completed Projects==
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===2023===
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category = Completed
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category = 2023
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===2022===
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===2021===
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category = 2021
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==Ongoing Projects==
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category = In progress
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category = IIP
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Latest revision as of 15:53, 8 January 2024


The Integrated Information Processing (IIP) Group carries out research in the following areas:


Theory, Algorithms, and Hardware for Beyond 5GPositioning with Wireless SignalsSimultaneous Sensing and CommunicationAll-Digital In-Memory ProcessingAnalog-to-Information Conversion for Low-Power SensingMixed-Signal Circuit DesignReal-Time OptimizationAudio Signal ProcessingIip presentation jan23.jpg


Theory, Algorithms, and Hardware for Beyond 5G

The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.

Positioning with Wireless Signals

Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.

Simultaneous Sensing and Communication

Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.

All-Digital In-Memory Processing

Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.

Analog-to-Information Conversion for Low-Power Sensing

Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.

Mixed-Signal Circuit Design

All-digital beamforming architectures for massive multi-antenna (MIMO) wireless systems provide best-in-class beamsteering capabilities and simplify many baseband processing tasks. The drawback of such architectures is the need for a large number of radio-frequency (RF) frontends (FEs), which can result in high power consumption and large silicon area. The projects in this area focus on jointly designing and optimizing mixed-signal RF circuits and digital baseband processing implementations to avoid the drawbacks of all-digital beamformers.

Real-Time Optimization

Numerical optimization finds use in a large number of fields, including wireless communications, machine learning, imaging, physics, operations research, and control. In a growing number of embedded applications, convex as well as nonconvex optimization problems must be solved in real-time and with stringent latency constraints. The projects in this area focus on the design of novel algorithms that enable real-time numerical optimization at low latency and in a hardware friendly manner.

Audio Signal Processing

Machine learning and deep neural networks are currently revolutionizing a variety of applications, including the well-established field of digital signal processing. The projects in this area focus on the design of novel algorithms that enable real-time audio signal processing using emerging tools from digital signal processing and machine learning. The ultimate goal is their realization on digital signal processors (DSPs) or hardware accelerators (FPGAs and ASICs).

Available Projects

Theory, Algorithms, and Hardware for Beyond 5G


Positioning with Wireless Signals


Simultaneous Sensing and Communication


All-Digital In-Memory Processing


Analog-to-Information Conversion for Low-Power Sensing


Mixed-Signal Circuit Design


Real-Time Optimization


Audio Signal Processing


Completed Projects

2023


2022


2021


Ongoing Projects