2021 QIF North America Program Details

APPLICATION:

Each team should submit an application by the specified deadline (see Timeline tab) that must include:

1. One page abstract of innovation proposal

2. Letter from one or two faculty members recommending the innovation
a. Why the proposal is innovative
b. Why the proposal is important
c. Why the current team is likely to succeed in their proposal

3. Each student's CV

4. Signed copy of QIF rules (both students)

 

SUBMISSIONS SHOULD BE IN A ZIP FILE

The submission portal will open two weeks before the deadline.

 

PROPOSAL PHASE:

Selected teams will be notified directly to participate in the next phase by submitting a final proposal that must include a Three-page innovation proposal (plus one for reference/bibliography) including:

a. Introduction and problem definition

b. Innovation proposal and relation to the state of the art

c. One-year horizon of the project, even if the proposal is a multi-year project

d. Strength of the team to achieve the proposal milestones

 

Please upload your proposal in zip format to the QIF submission portal for updates by the specified deadline. Use your access code received in the confirmation email when you submitted your abstract.

 

SELECTION OF FINALISTS AND WINNERS:

Finalist teams will be selected to participate in the final presentation phase of the Program. Each finalist team must prepare a presentation slide deck for the judges. Presentation must be in PowerPoint or PDF format. The presentation generally includes

• The idea
• The differentiating factors from state of the art
• The execution plan / strength of the team

Each Finalist team must also prepare a poster for a poster session to be presented to the judges.

Winning teams will be chosen from these finalists after they present to the judging panel.

 

ADDITIONAL INFO:

• Applications must be submitted online by the specified deadline (see Timeline tab). All proposals and presentations will be reviewed internally by a team of Qualcomm researchers.

• The official rules for the Qualcomm Innovation Fellowship are available for download and set forth the Program’s governing guidelines.

• All QIF-related information will be announced on this webpage. Please check back regularly for updates.

Qualcomm Research is a division of Qualcomm Technologies, Inc.

 

We invite teams to submit proposals in the following areas. We also welcome proposals outside of the sub-areas listed below:

Advanced Semiconductor Electronics

• Ultra-low (uW) power embedded platform for edge computing (ULP architectures and designs, HW accelerators, power generation and management, novel memories, security)
• Novel materials and heterogeneous integration (2D semiconductors, GaAs, GaN, etc.)
• CMOS package integration (thermal-aware designs or circuits, advanced packaging techniques, antenna-in-package etc.)
• RF / analog ASICs and architectures (Sub-6GHz 5G power amplifiers, mmWave RFIC and Data Converters for 5G NR, adaptive RF signal processing algorithms, etc.)
• Advanced antenna (mmWave and phase-array antennas), novel antenna materials, structures and implementations
• Power Management ASICs (wide bandwidth SMPS, wide bandwidth envelope tracker, embedded regulation)
• ASIC implementation methodology development for improved Performance, Power, Area, Yield. On-die power grid analysis and optimization, dynamic and static power optimizations, silicon-driven static timing analysis, design optimization for yield improvement
• Low-power high-speed Data Converters
• Low-power high-bandwidth SERDES for die to die and chip to chip communication
• Low-power high-bandwidth memory interface (DRAM and FLASH) circuits
• Ultra-low power ML HW design using mixed-signal techniques (such as Compute-In-Memory and analog neural network)
• Mixed-signal HW design using ML techniques
• Digital wireless transceivers
• Interference/jammer cancellation and concurrent transceivers/coexistence

 

Advances in Communication Techniques and Theory

• Reliable low latency communications for licensed/unlicensed in mid band and mmW spectrum
• Machine learning designs for wireless communications systems and algorithms
• Joint communication and sensing/positioning techniques for licensed and unlicensed spectrum
• Vehicle-vehicle, vehicle to infrastructure, and vehicle-pedestrian communications design
• Cooperative sensing and communication for radar and other sensors
• Low energy networks (Wi-Fi, Bluetooth LE, 802.15.4, Zigbee, etc.)
• Low-power signal-processing algorithms for mmW massive-MIMO communication systems
• Advanced low power HW/FW/SW modem implementation approaches
• New signal-processing techniques and use-cases using RF sensing (wireless channel capture)
• Iterative detection and decoding algorithms
• Non coherent communication for low power IoT
• Communication systems design in THz bands

 

• Advanced sensors and sensor fusion
• Imaging radar
• Computer vision for autonomy
• Sensor fusion with deep learning
• Behavior planning with uncertainty

 

• Natural language processing and voice user interface
• Computer vision and geometric deep learning
• Reinforcement and continual learning
• ML for combinatorial optimization
• Graph neural networks
• On-device training and model personalization
• Intermediate representation for machine learning workloads/compilers
• Transfer learning and Knowledge distillation
• Extreme energy efficient inference hardware accelerators for ML loads and lower complexity algorithms and convolutional nets
• Deep generative modeling and unsupervised learning
• Bayesian deep learning and uncertainty estimation
• Federated learning

 

• Real Time 3D perception, mapping, reconstruction, and geometry interpretation
• Eye-tracking devices and algorithms
• Hand skeleton and multimodal human body pose estimation and tracking
• Low power/complexity rendering systems
• Lighting/illumination modelling
• Multi-focal, near eye displays
• High efficiency video coding techniques
• Deep learning based image and video compression (intra and inter prediction, in-loop filters, transforms, entropy coding)
• Deep learning based optimized video encoding
• Perceptually optimized video coding
• Image and video quality assessment
• 6DoF video compression, Point Cloud compression
• Few-shot learning in human activity classification
• Dynamic texture and shape synthesis in the wild
• Learning universal networks for multiple tasks in computer vision, e.g. detecting, tracking, segmenting objects and scenes in videos

 

• Novel processor architectures, microarchitectures, extensions, and accelerators
• Novel processor architectures, microarchitectures, extensions, and accelerators
• Multimedia and gaming architectures (not limited to GPU, GPGPU, VLIW, DSP, etc.)
• Novel architectures for artificial intelligence, edge training, inference and processing in memory
• Security features of CPUs and accelerators at the instruction set, execution, storage (including caches) and SOC levels
• Machine learning CAD for VLSI HW design

 

• Isolation technologies: Virtualization, enclaves, and software sandboxing
• Key management for IoT: Establishing trust between embedded devices
• Machine learning model security against adversarial attacks
• Protocol security: Analysis and verification of communication protocols such as 3GPP for 5G to discover security issues both in standards and their implementations
• SoC security: Security of heterogeneous systems on chip
• Software-based exploitation of hardware vulnerabilities, such as micro-architectural, side-channels and glitch attacks, and proper countermeasures
• User authentication: Biometric and behavioral authentication of users by mobile and embedded devices
• Vulnerability detection: New tools and techniques for finding exploitable vulnerabilities in Software and/or Hardware, with a focus on embedded systems
• Solutions for data provenance, privacy and security

 

• 5G test techniques for mmWave and sub6 devices and system
• Defect-oriented testing and fault modeling in deep sub-micron process nodes
• Applications of Data Analytics, Machine Learning and AI in Test
• Test Challenges for 2.5D/3D Systems in Packages

Qualcomm is inviting applications for the Qualcomm Innovation Fellowship 2020 from PhD students in the Electrical Engineering and Computer Science (and related) departments at:

United States

• California Institute of Technology
• Carnegie Mellon University (CMU)
• Columbia University
• Cornell University
• Georgia Institute of Technology
• Massachusetts Institute of Technology (MIT)
• Princeton University
• Purdue University
• Rutgers University
• Stanford University
• UC Berkeley (UCB)
• UC Los Angeles (UCLA)
• UC San Diego (UCSD)
• UC Santa Barbara (UCSB)
• University of Illinois at Urbana-Champaign (UIUC)
• University of Maryland, College Park
• University of Michigan
• University of Southern California (USC)
• University of Texas at Austin
• University of Washington
• University of Wisconsin-Madison
• Virginia Polytechnic Institute and State University

Canada

• University of Montreal
• University of Toronto

Info Sessions are scheduled in October 2020 – details in the ‘Info Sessions’ section

 

Submission site opens: November 2020

Application submission deadline: December 1, 2020 (10:00AM PST)

Selection announcement: December 2020

Proposal submission deadline:  January 2021

Finalists’ announcement: March 2021

Finalist presentations submission deadline: March 2021

Finalist presentations: April 2021

Winners’ announcement: May 2021

QIF Winners’ Day: October 2021