We believe that research and development is the key to harnessing the power of imagination and to discovering new possibilities. We are excited to announce a new kind of Fellowship that promotes Qualcomm’s core values of innovation, execution and teamwork. Our goal is to enable students to pursue their futuristic innovative ideas.
|
School |
Students |
Recommender(s) |
Innovation Title |
|---|---|---|---|
|
CMU |
Xinshuo Weng, Ye Yuan |
Kris Kitani |
3D Multi-Agent Social Interaction Understanding and Diverse Future Behavior Forecasting for Next General AI Systems |
|
Columbia |
Mohamed Hassan, Evgeny Manzhosov |
Simha Sethumadhavan |
Practical Software Security on Heterogeneous System on Chips |
|
UCB |
Keertana Settaluri, Kourosh Hakhamaneshi |
Vladimir Stojanovic, Borivoje Nikolic |
Designing Analog Mixed Signal Circuits with Machine Learning |
|
UCSD |
Casey Hardy, Abdullah Abdulslam |
Hanh-Phuc Le, Patrick Mercier |
Vertical Hybrid Power Delivery for High-Performance Processors and Digital Systems |
|
UCSD |
Minghua Liu, Xiaoshuai Zhang |
Hao Su |
Learning-Based 3D Mesh Reconstruction |
|
UCSD |
Shuyang Li, Bodhisattwa Prasad Majumder |
Julian McAuley |
Toward Personalized and Multimodal Conversational Recommender Systems |
|
UCSD |
Sai Bi, Zhengqin Li |
Manmohan Chandraker, |
Physically-Motivated Deep Inverse Rendering from Sparse Inputs |
|
UCSD |
Dominique Meyer, Hengyuang Zhang |
Henrik Christensen |
Temporospatial Fusion of Radar, Vision and LIDAR data for Autonomous Driving |
|
UIUC |
Suraj Jog, Junfeng Guan |
Haitham Hassanieh |
High Resolution Millimeter Wave Imaging Using Deep Adversarial Learning |
|
USC |
Runzhou Zhang, Huibin Zhou |
Alan E. Willner |
High-Capacity Mode-Division-Multiplexed Wireless Communications Within and Beyond Millimeter-Wave Band |
|
UT Austin |
Khurram Mazher, Andrew Graff |
Robert W. Heath Jr., |
Radar-to-radar interference: System level analysis and solutions |
|
Virginia Tech |
Yibin Liang, Kangjun Bai |
Yang (Cindy) Yi, |
A Low-Power Hybrid Neural Processing Architecture for Mobile Edge Intelligent Computing |
|
Washington |
John Thickstun, Vivek Jayaram |
Sham Kakade |
Source Separation with Deep Generative Priors |
The following 42 proposals have been selected as Finalists. Congratulations!
| University | Innovation Title | Students |
| CMU | Hardware-Aware Multimodal 3D Object Detection for On-Device Augmented Reality Applications | Ting-Wu Chin, Ahmet Inci |
| CMU | Safe Adaptive Learning and Control for Dynamical Systems | Charles Noren, Weiye Zhao |
| CMU | 3D Multi-Agent Social Interaction Understanding and Diverse Future Behavior Forecasting for Next General AI Systems | Xinshuo Weng, Ye Yuan |
| CMU | Ad Hoc Spatially-Anchored Augmented Reality Interfaces | Karan Ahuja, Sujeath Pareddy |
| Columbia | Protecting Heterogeneous SoCs with Security Sockets | Davide Giri, Luca Piccolboni |
| Columbia | Practical Software Security on Heterogeneous System on Chips | Mohamed Hassan, Evgeny Manzhosov |
| Cornell | Synthesis-Driven ISA Extensions for DSPs | Alexa VanHattum, Rachit Nigam |
| Georgia Tech | Towards Building Machine-Learning Powered EDA Flow and Methodologies | Anthony Agnesina, Yi-Chen Lu |
| MIT | Side-Channel Security Analysis of Embedded Machine Learning Implementations and Efficient Software / Hardware Countermeasures | 3Saurav Maji, Utsav Banerjee |
| MIT | Aerial Drone Sensing and Intelligence | Favyen Bastani, Songtao He |
| MIT | Privacy-Preserving Video Analytics with Untrusted Queries | Francis Cangialosi, Neil Agarwal |
| Princeton | Exploring In-Memory Computing for Architectural and Technology Scaling | Peter Deaville, Rakshit Pathak |
| Purdue University | EM/Power Statistical and Machine-Learning Side-Channel Attacks & Generic Low-Overhead Synthesizable Circuit-Level Countermeasures | Debayan Das, Baibhab Chatterjee |
| Purdue University | Enabling Edge Intelligence with In-Memory Accelerators for Ultra-low Precision Deep Neural Networks | Niharika Thakuria, Sourjya Roy |
| Purdue University | Toward energy-efficient and accurate in-memory analog computing systems for machine learning workloads | Indranil Chakraborty, Mustafa Fayez Ahmed Ali |
| Stanford | Peer Pressure: On-Device Learning from Soft Decisions | Ilai Bistritz, Ariana Mann |
| Stanford | Autoregressive Generation that Adapts to Computational Constraints | Yang Song, Rui Shu |
| UCB | Designing Analog Mixed Signal Circuits with Machine Learning | Keertana Settaluri, Kourosh Hakhamaneshi |
| UCLA | Robust Crossbar Persistent Memories for In-Memory Learning Acceleration | Zehui Chen, Siyi Yang |
| UCLA | Autonomous Driving with Smartphones using Online Expectation-Maximization and Controllable Stereoscopic Vision | Tsang-Kai Chang, Kenny Chen |
| UCSB | N-face InGaN/AlGaN RF power HEMTs with a relaxed InGaN channel for mm-wave power amplifier | Shubhra Pasayat, Weiyi Li |
| UCSB | Learning to Encourage Cooperative Behavior in Multi-agent Systems | Daniel Lazar, Erdem Biyik |
| UCSB | Practical algorithms for near-capacity massive MAC: Iterative schemes based on principled convex-relaxations | Ganesh Ramachandra Kini, Orestis Paraskevas |
| UCSD | LEGOS: AI for Cross-Domain Multi-Tenant Acceleration of Autonomous Systems | Byung Hoon Ahn, Soroush Ghodrati |
| UCSD | Physically-Motivated Deep Inverse Rendering from Sparse Inputs | Sai Bi, Zhengqin Li |
| UCSD | Vertical Hybrid Power Delivery for High-Performance Processors and Digital Systems | Casey Hardy, Abdullah Abdulslam |
| UCSD | Finding and Eliminating Timing Side-Channels in Crypto Code with Pitchfork | Sunjay Cauligi, Craig Disselkoen |
| UCSD | Temporospatial Fusion of Radar, Vision and LIDAR data for Autonomous Driving | Dominique Meyer, Hengyuang Zhang |
| UCSD | Doing More with Less:Sparse Sensing for Millimeter Wave Channel Estimation | Pulak Sarangi, Rohan Ramchandra Pote |
| UCSD | Learning-Based 3D Mesh Reconstruction | Minghua Liu, Xiaoshuai Zhang |
| UCSD | Toward Personalized and Multimodal Conversational Recommender Systems | Shuyang Li, Bodhisattwa Prasad Majumder |
| UIUC | High Resolution Millimeter Wave Imaging Using Deep Adversarial Learning | Suraj Jog, Junfeng Guan |
| UMD | Provably robust neural networks using curvature regularization | Sahil Singla, Yogesh Balaji |
| USC | High-Capacity Mode-Division-Multiplexed Wireless Communications Within and Beyond Millimeter-Wave Band | Runzhou Zhang, Huibin Zhou |
| UT Austin | mmWave and TeraHz Channel Estimation and Beamforming using Deep Generative Networks | Akash Doshi, Ajil Jalal |
| UT Austin | Radar-to-radar interference: System level analysis and solutions | Khurram Mazher, Andrew Graff |
| Virginia Tech | Physics-Driven Machine Learning and Data Fusion for Semiconductor Test, Quality and Yield Learning | Yinan Wang, Tim Lutz |
| Virginia Tech | A Low-Power Hybrid Neural Processing Architecture for Mobile Edge Intelligent Computing | Yibin Liang, Kangjun Bai |
| Virginia Tech | Hybrid Reinforcement Learning for Autonomous Vehicles in Adversarial Environments | Ian Garrett, Leila Amanzadeh |
| Washington | Source Separation with Deep Generative Priors | John Thickstun, Vivek Jayaram |
| Wisconsin-Madison | Comprehensively Accelerating Sequence-based Neural Networks | Preyesh Dalmia, Suchita Pati |
| Wisconsin-Madison | Ultra Low-Power Machine Learning at the Edge | Tianen Chen, Setareh Behroozi |
The Finals presentations are a major part of the winner selection. Both members of each finalist team are expected to present at the Finals.
Prepare a 12 - minute presentation
You must submit your presentation slides in PDF and PowerPoint format, a video-recording of your presentation and poster in PDF only format by 10:00AM PST on Thursday May 14th, 2020.
The submission site will be open on May 1st, 2020. Details of the format of the slides and poster are below. Use your assigned confirmation number for the submission.
SUBMISSION SHOULD BE A SINGLE ZIP FILE CONTAINING THE THREE OR FOUR DOCUMENTS: PRESENTATION SLIDES, PDF OF SLIDES _FORPRINT, VIDEO RECORDING, FIRST POSTER, AND OPTIONAL SECOND POSTER.
12-slides maximum, including the cover page. The presentation slides generally include:
The idea
The differentiating factors from state of the art
The execution plan and strength of the team
We do not recommend adding slides with references. If you do, they would also count towards the 12-slide limit.
Please do not use animations purely to get around the 12-slide limit.
Please ensure that all external media is embedded in the PDF or PowerPoint file itself, e.g., videos. The PDF/PowerPoint file must be self-contained.
In addition to your presentation slides, please provide a for-print PDF (8.5"X11" landscape) of your presentation (max. 12 pages). The name of the for-print PDF should end with "_forprint.pdf". It should have the same content (sans animations) as your presentation.
Each team may submit up to two posters (PDF format required, for-print 24"x36").
The submitted posters will be used for the interactive session with the judges, which is a part of the Finals event.
We received 188 abstracts this year. After a careful selection process, the following have been chosen to proceed to the Proposal phase of the QIF 2020. Congratulations!
| University | Submission No. | Innovation Title |
| CMU | S2019-15933 | Resource-Efficient Distributed Statistical Inference in Networks |
| CMU | S2019-15947 | Hardware-Aware Multimodal 3D Object Detection for On-Device Augmented Reality Applications |
| CMU | S2019-15998 | Developing a Programmable RRAM-Based Platform for High-Performance and High-Efficiency Processing-in-Memory |
| CMU | S2019-16019 | Redundancy-driven neural compression |
| CMU | S2019-16039 | Safe Adaptive Learning and Control for Dynamical Systems |
| CMU | S2019-16054 | A Highly Energy Efficient TNN-based Sensory Processing Unit for Image Segmentation |
| CMU | S2019-16076 | Re-imagining the system stack for ultra-low-power image sensing |
| CMU | S2019-16087 | Generating Free-form Animations by Coalescing Narrative and Dialogue |
| CMU | S2019-16092 | 3D Multi-Agent Social Interaction Understanding and Diverse Future Behavior Forecasting for Next General AI Systems |
| CMU | S2019-16100 | Ad Hoc Spatially-Anchored Augmented Reality Interfaces |
| Columbia | S2019-15943 | Deep Unfolded Neural Network for Wireless Communication: Algorithm and Hardware Co-design |
| Columbia | S2019-16010 | Protecting Heterogeneous SoCs with Security Sockets |
| Columbia | S2019-16052 | Adversarial Learning for Typical Nuisances |
| Columbia | S2019-16095 | Practical Software Security on Heterogeneous System on Chips |
| Cornell | S2019-16050 | Synthesis-Driven ISA Extensions for DSPs |
| Georgia Tech | S2019-15959 | Privacy-Preserving and Attack-Resilient Federated Learning |
| Georgia Tech | S2019-16049 | Evolving architecture recommendation system for custom hardware design |
| Georgia Tech | S2019-16111 | Towards Building Machine-Learning Powered EDA Flow and Methodologies |
| Michigan | S2019-16014 | A Deep Neural Network Assisted Terahertz Communication Link with Rapid In-Field Performance Optimization |
| Michigan | S2019-16094 | Energy Efficient Biologically Inspired Embedded Machine Vision |
| MIT | S2019-15940 | Side-Channel Security Analysis of Embedded Machine Learning Implementations and Efficient Software / Hardware Countermeasures |
| MIT | S2019-15992 | Distributed Ledgers On the “Edge” for Trustworthy Cellular-V2X |
| MIT | S2019-16001 | Aerial Drone Sensing and Intelligence |
| MIT | S2019-16091 | Learning Transferable Strategies via Skills and Memory |
| MIT | S2019-16105 | Privacy-Preserving Video Analytics with Untrusted Queries |
| MIT | S2019-16110 | Learning Generalizable and Scalable Reinforcement Learning Agents for Transportation Systems |
| Princeton | S2019-15941 | Exploring In-Memory Computing for Architectural and Technology Scaling |
| Princeton | S2019-15950 | Exploiting Channels for Energy-efficient Physical Layer Security with Spatio-Temporal Modulated Arrays in Future mm-Wave Wireless Links |
| Purdue University | S2019-15929 | Systematic Security Analysis of Narrowband Internet of Things (NB-IoT) and LTE-M |
| Purdue University | S2019-15951 | EM/Power Statistical and Machine-Learning Side-Channel Attacks & Generic Low-Overhead Synthesizable Circuit-Level Countermeasures |
| Purdue University | S2019-16007 | Hardware Security with Embedded Spintronics for Internet of Things |
| Purdue University | S2019-16018 | Enabling Edge Intelligence with In-Memory Accelerators for Ultra-low Precision Deep Neural Networks |
| Purdue University | S2019-16021 | Optimization of Autonomous Vehicle Routes with Flexible Objectives |
| Purdue University | S2019-16036 | Toward energy-efficient and accurate in-memory analog computing systems for machine learning workloads |
| Purdue University | S2019-16048 | MixNN: Significance based Tensor Bit-Width Allocation using PCA for Edge Inference |
| Purdue University | S2019-16051 | Accelerating Irregular Machine Learning Models |
| Purdue University | S2019-16060 | Hardware-Aware Adversarial Attacks on Deep Neural Networks |
| Purdue University | S2019-16069 | Detecting Deepfakes Using Audio-Video Inconsistencies |
| Purdue University | S2019-16080 | Next-generation Hardware Enclaves |
| Stanford | S2019-15995 | Peer Pressure: On-Device Learning from Soft Decisions |
| Stanford | S2019-16029 | Autoregressive Generation that Adapts to Computational Constraints |
| UCB | S2019-15956 | Monolithic Fast-Scanning Lidar through Wavevector-Division Multiplexing |
| UCB | S2019-15957 | High-Speed Spatial Light Modulators for Holographic Near Eye Displays |
| UCB | S2019-15963 | Real-World Scalability of Zero-knowledge Proofs |
| UCB | S2019-15979 | Interactive Computer Vision Techniques for Authoring AR Tutorials |
| UCB | S2019-16063 | Designing Analog Mixed Signal Circuits with Machine Learning |
| UCB | S2019-16116 | Hardware-Software Co-Design for Agile Verification of Trusted Execution Environments |
| UCLA | S2019-15946 | Turning Any Object into Robots |
| UCLA | S2019-15990 | Microgesture: Human-computer Interaction at the Micro Scale |
| UCLA | S2019-16017 | Ultra-High-Resolution Transportation Mode Recognition and Localization using Phonon-Engineered Optomechanical Inertial Sensors |
| UCLA | S2019-16057 | Robust Crossbar Persistent Memories for In-Memory Learning Acceleration |
| UCLA | S2019-16113 | Autonomous Driving with Smartphones using Online Expectation-Maximization and Controllable Stereoscopic Vision |
| UCSB | S2019-15931 | Stochastic Bounds for Robust Adversarial Learning in Vehicle Navigation |
| UCSB | S2019-15952 | N-face InGaN/AlGaN RF power HEMTs with a relaxed InGaN channel for mm-wave power amplifier |
| UCSB | S2019-15969 | Learning to Encourage Cooperative Behavior in Multi-agent Systems |
| UCSB | S2019-15989 | Practical algorithms for near-capacity massive MAC: Iterative schemes based on principled convex-relaxations |
| UCSB | S2019-16015 | Efficient, Robust, and Private Federated Learning |
| UCSB | S2019-16037 | Reinforcement Learning for Safety-Critical Systems |
| UCSB | S2019-16042 | Energy-Efficient and End-to-End Training of Tensorized Neural Networks on Resource-Constrained Platforms |
| UCSB | S2019-16102 | Device-free Cardiac Activity Monitoring Via WiGig |
| UCSB | S2019-16106 | Machine Learning Approaches for High Average Efficiency Power Amplifier Design |
| UCSD | S2019-15935 | LEGOS: AI for Cross-Domain Multi-Tenant Acceleration of Autonomous Systems |
| UCSD | S2019-15937 | Physically-Motivated Deep Inverse Rendering from Sparse Inputs |
| UCSD | S2019-15954 | mmTags: A Low-Cost Solution to Enhance Radar Vision |
| UCSD | S2019-15962 | Robust and Efficient MIMO mmWave Communication for Systems with Nonlinearities |
| UCSD | S2019-15966 | Intent Recognition for Pedestrians and Cars |
| UCSD | S2019-15975 | Automated Generation of 3D Labeled Human Pose and Motion Dataset |
| UCSD | S2019-15978 | Effective deep learning techniques for wireless communication |
| UCSD | S2019-15983 | CommRad: Concurrent Communication and Radar for Vehicles |
| UCSD | S2019-15985 | DeepPHY: Towards Secure Physical Layer Communications using Deep Learning |
| UCSD | S2019-15988 | Real-Time Reinforcement Learning on Edge Devices |
| UCSD | S2019-16004 | W-air glasses - Non-invasive continuous respiratory sensing device |
| UCSD | S2019-16031 | A Decentralized Bayesian Learning Framework for Supervised and Unsupervised Learning |
| UCSD | S2019-16035 | Vertical Hybrid Power Delivery for High-Performance Processors and Digital Systems |
| UCSD | S2019-16047 | Controllable Speech Synthesis using Hardware Software Co-design |
| UCSD | S2019-16064 | Finding and Eliminating Timing Side-Channels in Crypto Code with Pitchfork |
| UCSD | S2019-16077 | Temporospatial Fusion of Radar, Vision and LIDAR data for Autonomous Driving |
| UCSD | S2019-16084 | Doing More with Less:Sparse Sensing for Millimeter Wave Channel Estimation |
| UCSD | S2019-16089 | Learning-Based 3D Mesh Reconstruction |
| UCSD | S2019-16109 | Toward Personalized and Multimodal Conversational Recommender Systems |
| UCSD | S2019-16114 | Context Assisted Robust Localization using Wireless Sensing |
| UIUC | S2019-15942 | Agile RF Front-Ends Using Lithium Niobate MEMS Filters and N-Path Switched-LC Circuits |
| UIUC | S2019-15997 | Hardware Accelerators for Novel Real-time Forecasting Algorithms |
| UIUC | S2019-16012 | Protecting keys in the wild using Modern Cryptography |
| UIUC | S2019-16086 | High Resolution Millimeter Wave Imaging Using Deep Adversarial Learning |
| UMD | S2019-15930 | Provably robust neural networks using curvature regularization |
| UMD | S2019-15948 | PRGFlyt: Unified Active Perception Architecture for UAV Autonomy |
| University of Toronto | S2019-15932 | Analysis and Rehabilitation of Hand Function Using Egocentric Video |
| University of Toronto | S2019-15934 | Emo-PupilNet: A Deep Generative Model for Continuous Behavior Analysis using Pupillometry |
| University of Toronto | S2019-16000 | An Inverse Reinforcement Learning-Based Approach to Smart Traffic Management System |
| University of Toronto | S2019-16068 | Guided deep learning for the prediction of brain activation patterns in a driver for handoff in semi-autonomous vehicles |
| University of Toronto | S2019-16081 | Active Learning for Generative Adversarial Networks in Cybersecurity |
| USC | S2019-15964 | Learning Human-Aware Manipulation Plans by Watching YouTube Videos |
| USC | S2019-15976 | High-Capacity Mode-Division-Multiplexed Wireless Communications Within and Beyond Millimeter-Wave Band |
| USC | S2019-16030 | FLWiEN: A Fast, Robust and Secure Framework for Federated Learning in Wireless Edge Networks |
| USC | S2019-16046 | Energy and Area Efficient Beyond CMOS CNN Accelerators Through Architecture Specific Sensitivity-driven Pruning |
| USC | S2019-16112 | Data Trustworthiness Quantification and Propagation in Self-driving Car Systems |
| USC | S2019-16115 | Specification and Formal Verification of Federated Machine Learning Systems |
| UT Austin | S2019-15996 | mmWave and TeraHz Channel Estimation and Beamforming using Deep Generative Networks |
| UT Austin | S2019-16022 | Radar-to-radar interference: System level analysis and solutions |
| Virginia Tech | S2019-15928 | Physics-Driven Machine Learning and Data Fusion for Semiconductor Test, Quality and Yield Learning |
| Virginia Tech | S2019-15953 | Adaptive Integrated Power Supply with Neural Network Based Learning and Control for IoT Edge Computing Applications |
| Virginia Tech | S2019-15972 | Experienced Deep Reinforcement Learning for Reliable Communications |
| Virginia Tech | S2019-15991 | A Low-Power Hybrid Neural Processing Architecture for Mobile Edge Intelligent Computing |
| Virginia Tech | S2019-16009 | Real-time Detection and Adaptive Mitigation of Side-Channel Leakage in SoC |
| Virginia Tech | S2019-16070 | Designing Adaptive Information Display and Access Techniques for All-Day Wearable AR Glasses |
| Virginia Tech | S2019-16079 | Hardware Acceleration for OS-level Memory Compression |
| Virginia Tech | S2019-16098 | Dynamic spectrum sharing by Distributed Reinforcement Learning on Mobile Edges |
| Virginia Tech | S2019-16104 | Hybrid Reinforcement Learning for Autonomous Vehicles in Adversarial Environments |
| Washington | S2019-16041 | Source Separation with Deep Generative Priors |
| Washington | S2019-16083 | Gatekeeper: A low-power wake-up module for wireless transceivers |
| Washington | S2019-16118 | Automating Tensor Program Synthesis for Novel Architecture |
| Wisconsin-Madison | S2019-15999 | Accelerating Machine Learning Inference with Approximate Caching on Accelerators |
| Wisconsin-Madison | S2019-16062 | Comprehensively Accelerating Sequence-based Neural Networks |
| Wisconsin-Madison | S2019-16066 | Ultra Low-Power Machine Learning at the Edge |
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 and make a 12-minute video recording of them presenting the slides for the judges. Presentation must be in PowerPoint or PDF format. The presentation generally includes:
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.
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:
• 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 (millimeter-wave 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
• Reliable low latency communications for low unlicensed and mmWave spectrum
• Machine learning designs for wireless communications systems and algorithms
• Advanced communication and positioning techniques for licensed and unlicensed spectrum
• Vehicle-vehicle and vehicle-pedestrian communications design
• 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
• Advanced sensors and sensor fusion
• Imaging radar
• Computer vision for autonomy
• Sensor fusion with deep learning
• Behavior planning with uncertainty
• Natural language processing
• Computer vision
• Reinforcement and continual learning
• On-device training
• Intermediate representation for machine learning workloads/compilers
• Transfer learning and Knowledge distillation
• Novel compute architectures for ML tasks, e.g. in-memory compute, analog compute
• 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 and quantum machine 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
• 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: DRM for learning models
• Protocol security: Analysis and verification of communication protocols
• SoC security: Security of heterogeneous systems on chip
• Software-based exploitation of hardware vulnerabilities: Micro-architectural attacks, side-channels, and associated 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, Trojans and Backdoors, with a focus on embedded systems
• Solutions for data provenance, privacy and security
• 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
Canada
Due to COVID-19 national emergency, the Finals event is postponed until further notice. Please stay tuned for updates.
Submission site opens: November 12, 2019
Application submission deadline: December 9, 2019 (10:00AM PST)
Selection announcement: December 19, 2019
Proposal submission deadline: January 27, 2020 (10:00AM PST)
Finalists’ announcement: March 30, 2020
Finalist presentations submission deadline: May 14, 2020 (10:00 AM PST)
Finalist presentations: Postponed Due to COVID-19
Winners’ announcement: Postponed Due to COVID-19
QIF Winners’ Day: October 2020
The Qualcomm Innovation Fellowship began in 2009, and has continued to grow with the addition of more universities, more candidates, and expansion to our research centers internationally. Take a look at a list of all our fellowship winners and finalists from years past:
Qualcomm Innovation Fellowship Finalists' Day
Oct 19, 2020
1:35
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