Seedling Information

There are a variety of opportunities to engage with IARPA, and a seedling is another option to receive funding for your ideas. Seedlings are typically 9-12 month research efforts intended to “take an idea from disbelief to doubt,” or rather, to help to determine whether a full research program (lasting 3-5 years) is warranted. Most seedlings stem from proposals received through the General Broad Agency Announcements that are released annually.

Submit Your Idea

IARPA Distribution and Evaluation System (IDEAS) is a web-based software tool that enables online submission of unclassified proposals submitted in response to IARPA research solicitations.

If you are interested in learning more or have questions about a particular seedling, please contact (301) 851-7500 or dni-iarpa-info@iarpa.gov.

Previous Seedlings:

 

Analysis

NameDescription
Algorithms for Learning Using Privileged Information
In a Learning Using Privileged Information (LUPI) model, some of the training examples are augmented with additional comments, comparisons, or other useful information.

This additional information has been shown theoretically to speed up the rate of convergence for learning. In a recent study, LUPI-based Support Vector Machines (SVM+) were found to achieve the same or better accuracy as an SVM model with a significant reduction in the amount of training data required. One challenge that this approach faces is that it requires 10 times the computation time as an SVM, which could reduce the practicality of the approach for many applications. The goal of the seedling is to reduce computation time to SVM levels without eliminating the benefits of the SVM+ approach and to further explore the types of privileged information that could benefit the Intelligence Community.

Performer: Applied Communication Sciences

Resulting Research: FA8650-11-C-7161
Augmented Reality-based Collaborative and Corroborative Collection (ARC3)
The Augmented Reality-based Collaborative and Corroborative Collection (ARC3) seedling is an effort to analyze the requirements for designing, building and demonstrating an augmented-reality system that will work seamlessly with humans to collaboratively and corroboratively collect information in dynamic real-world environments.

As part of this effort, the performer (SRI International) will (1) study key needs, specifications, high-risk areas and potential approaches to building an end-to-end ARC3 system; (2) empirically study the implications of a dynamic information aperture with respect to trade-offs between efficiency, usability and reliability when automated, versus user-driven approaches are followed. This will be accomplished by developing experimental prototype interfaces for AR collaboration and corroboration between an analyst and a collector; and (3) empirically study techniques for automated and user-driven information extraction in the presence of uncertain and unreliable visual localization and scene understanding technologies. An experimental prototype will build user scenarios in which the interplay between audio-visual-text processing and user interaction can be systematically evaluated.

Performer: Johns Hopkins University Applied Physics Laboratory, SRI International

Resulting Research: 2012-120508000010 OR FA8650-14-C-7430
Automated Location Identification
This seedling will explore automatic methods to determine the geographic location of a scene that has been recorded in video and still images.

A unique aspect of this work is the utilization of existing publicly-tagged data to create a hierarchical tree model of a region structured by city, neighborhood, and block and leverage that information for search and retrieval. This seedling’s results support the Finder program.

Performer: University of Central Florida

Resulting Research: N10PC20231
Automatic Speech Recognition (ASR)
This research project proposes to lay the groundwork for future research in Automatic Speech Recognition (ASR) technology by quantitatively and qualitatively exploring what is wrong with ASR.

The researchers plan to quantitatively analyze the sources of failure in current acoustic modeling approaches. The focus of the ASR research community has been on improving performance with increasing amounts of matched-condition training data and language modeling. Using a diagnostic approach, their prior research has revealed that many errors are due to mismatch between the data and the statistical assumptions built into the acoustic models. A key idea in the current effort is to use resampling at different levels to systematically modify the data in order to selectively match particular assumptions in the model. This will be used to discern specific error conditions and the extent of their contribution to overall error rate as well as providing a quantitative metric for comparing data sets with regard to factors affecting performance. An important aspect of this research is training/test mismatch (a condition in which current ASR systems fail quite badly). In addition, the research team will ask experts in the field where they believe ASR technology is effective, where it fails, and what its shortcomings are. This survey will include both interviews and a review of the literature. This research effort will produce new quantitative methods and metrics for ASR acoustic model failure analysis, a report on failure modes and lessons learned, and suggestions for future research. The software developed in the research will be made freely available to the research community.

Performer: SRI International

Resulting Research: FA8650-15-C-9101

Automating Statistical Model Discovery (ASMD)
The seedling seeks to: 1) Establish the potential feasibility and real-world utility of intelligent data analysis tools that use automatic model discovery,

2) Define quantitative metrics and qualitative mechanisms for assessing and improving the quality of automatically built models, 3) Idenfity the problem classes, data types, and model families for which automating model discovery is likely to be feasible, and 4) Demonstrate infrastructure that makes it cost-effective to integrate automatic and manual model discovery approaches.

Performer: Massachusetts Institute of Technology

Resulting Research: 2015-15061000003

Collapse Immune Collaboration Environment for Research Oversight (CICERO)
Use computational models to explore the interaction dynamics of (simulated) groups of collaborating intelligence analysts.

The goal is to use the models to examine under what conditions groups are more likely to succumb to "cognitive collapse," and what interventions if any might be used to steer groups away from this undesirable state. "Cognitive collapse" refers to a state in which a group becomes fixated on a single hypothesis, failing to take into account contradictory evidence or to consider alternative hypotheses. The seedling completed at the end of 2QFY2011, with results demonstrating that the simulation-based tools provide a viable approach to exploring this type of group behaviors.

Performer: Jacobs Strategic Solutions Group, Inc.

Resulting Research: W911NF-10-C-0108
Data Engine for an Analyst's Workbench
Develop a high-performance and general-purpose Data Engine that will enable a workbench to operate on very large amounts of uncertain data, and that will provide the Inference Engine with just the data it needs in a usable form.

The proposed Data Engine has three key features. First, the engine will store and manage uncertain data, so uncertainty will be an integral part of its data model and algorithms. Second, the engine will be general purpose, meaning that the engine can be used in a variety of workbench settings, by many different inferencing algorithms, and by different applications. Thus, the engine will support a data model, query language, and interface that are appropriate for a wide spectrum of needs. Third, the engine will be high performance and it will be able to efficiently process vast amounts of data, often residing on disk or slow memory. The user will have the option of accepting approximate results in order to improve running times further.
Language Computer Corporation is exploring the automatic identification and characterization of emerging events as reported in textual information feeds, such as news reports. Complex events of the sort of interest to the IC are made up of multiple component events and contextual influences which over time can converge into an action or event of interest. By identifying component events and tracking them over time, event structures can emerge, supporting indicators and warnings to analysts as an input to their task prioritization process.

Performer: Stanford University

Resulting Research: N10PC20259

Dynamic Deep Displays
In 2011, the Dynamic Deep Displays seedling successfully demonstrated feasibility of photo-refractive materials for update-able holographic displays, and over its 12-month period increased brightness, image speed and energy efficiency all by a factor of 10.

Because of its compelling implications (no glasses required) to the future of 3D displays, Innovation Magazine identified this technology in the top three developments of 2010, right behind the iPad. The seedling permitted characterization of performance requirements for holographic displays allowing collaborative analysis with no head-gear, and the quantification of many metrics that formed the Synthetic Holographic Observation program. This is a good example of a seedling being used in the creation of a program.

Performer: Sandia National Lab

Resulting Research: N/A

Embodied Common Sense in Vision and Language
eCortex, Inc. explored the hypothesis that when a computer vision system patterned after the visual system of the mammalian brain is trained on realistic inputs (e.g., 3D imagery and video data) as opposed to the simpler inputs typically used (e.g., static 2D images), it will automatically learn useful “common sense” rules for recognizing objects in cluttered scenes.

Indeed, the results showed that, after the system learned the depth cues from realistic 3D imagery, performance increased for 2D imagery where the system successfully applied the 3D depth cues absent in the 2D images. These insights may guide the development of significantly more sophisticated automated image analysis systems.

Performer: eCortex, Inc.

Resulting Research: W911NF-10-C-0084
Explaining the Unseen (ETU)

This goal of this project is to enable a visual understanding of unfamiliar objects using representations that can transfer knowledge and provide sensible linguistic descriptions of the knowledge transfer.

Performer: Carnegie Mellon University

Resulting Research: 2016-16032900006
Measuring Dyadic Interactions
The Charles Stark Draper Laboratory is conducting the Measuring Dyadic Interactions seedling to investigate novel approaches for analyzing physiological data collected during near-real world, face-to-face dyadic interactions.

This research builds on a body of research that suggests there are indicators of behavioral and even physiological synchrony that emerge during such interactions and can provide clues to the quality of the exchange and eventual outcomes. Application of this research remains limited because it has primarily used subjective measures of behavior, relied on methods that have low temporal precision, and often treats each member of a dyad as an individual, rather than analyzing the interaction as a pair. Draper will seek to advance this research by applying linear and non-linear methods to analyze physiological and non-verbal data on a millisecond time frame (such as changes in the heart rate of the interviewer and interviewee relative to one another) as each person responds to and interacts with their dyadic partner.

Performer: Charles Stark Draper Laboratory, Inc.

Resulting Research: FA8659-12-C-7210
Meme Epidemiology
Develop a framework for understanding how ideas, particularly extremist ideas, are spread throughout a population.

The framework will be based on epidemiological methods used in understanding how infectious diseases are spread throughout a population. The goal is to develop proof-of-concept software tools that can identify the characteristics of “infectious” concepts of extremism/radicalization and how they morph through Internet blogs. The intent is to model how infectious ideas spread and how they evolve over time. Support vector machines will be trained to recognize concepts through the use of words and expressions and then analyze additional textual information (blogs) to track how the concept is replicated.

Performer: SRI International

Resulting Research: FA8650-11-C-7119
Meta-learning Strategies
Dartmouth College explores the potential of automated selection and parameterization of machine learning techniques to dramatically improve system performance across diverse problem areas.

The research could lay the groundwork for a larger program to automate the management of machine learning algorithms, resulting in dramatic reductions in the need for human experts when working with large, diverse data sets.

Performer: Dartmouth College

Resulting Research: N10PC20221
Model Capacity

The goal of this project is to enable mathematical and software tools for developers to create machine learning systems that consume far less labeled data, to quantify the labeled data requirements for a system early in its development, and to get feedback about how proposed changes to a machine learning model will affect labeled data requirements.

Performer: Gamelan Labs, Inc.

Resulting Research: 2016-16032900007

Sensors for Predicting Enhanced Language Learning
The Intelligence Community has a growing need for individuals across all career paths that have advanced foreign language skills.

While the IC invests heavily in training advanced language skills, research has demonstrated that language proficiency in adult language learners often does not achieve advanced levels of proficiency and even with the same training proficiency will vary greatly across different adult students. Recent research suggests that general cognitive abilities, brain structure and function may predict language learning outcomes; however, this has never been tested in healthy adults. The Sensors for Predicting Enhanced Language Learning seedling will seek to build upon previous findings to determine if there are cognitive and/or neurophysiological predictors of individuals who are more likely to be successful in learning and retaining a tonal-based novel language. The Massachusetts Institute of Technology research team will assess if there are pre-training features that predict the level of language acquisition throughout the language training, directly following completion, and at a 3 month follow-up.

Performer: Massachusetts Institute of Technology

Resulting Research: FA8650-12-C-7265
Study: Neurally Integrated Imagery Databases
The goal is to develop algorithms for decoding patterns of brain activity (brain signals) that can then query large databases of labeled/indexed images.

The idea is to develop a system whereby a cooperative subject would be placed in a non-invasive device (e.g., fMRI) and simply think about an image. The system would then reconstruct the image, identify appropriate attributes, and then search large image databases for similar images. The deliverables would be reports, analyses, and research data (state-of-the-art, program formulation, experimental design, maybe some experimental results). Intelligence analysts would benefit from simply thinking of an image and having a system retrieve similar images rather than entering in image attributes. There would be a significant reduction in image search and retrieval times.

Performer: Johns Hopkins University Applied Physics Laboratory

Resulting Research: N00024-03-D-6606
Technical Knowledge Acquisition (TechKnAcq)
The TechKnAcq seedling seeks to develop and explore a methodology that allows a user to submit queries the the TechKnAcq-developed software on concepts of interest, and to receive an ordered reading list of documents (or other media) that would facilitate knowledge acquisition by mimicking a structured curriculum.

If successful, the methodology is likely to power new solutions that revolutionize the productivity of the analytical enterprise and the precision and completeness of analytical products.

Performer: University of Southern California

Resulting Research: FA8650-15-C-9102

Anticipatory Intelligence

NameDescription
Agnostic Detection
Leidos, Inc. is exploring methods for the agnostic detection of genetically engineered mammalian organisms.

Recent advances in biotechnology have enabled the rapid, facile engineering of a diversity of biological systems. While these technologies have great promise, they also increase the risk of either accidental or purposeful misuse, and there are currently few methods to detect many sophisticated modifications. This seedling seeks to develop approaches for detecting signatures of biological engineering where the modification method, transgene sequence, and genomic target site(s) are unknown, including current methods of altering gene expression without changing DNA sequence.

Performer: Leidos, Inc.

Resulting Research: 2016-16071200002

DYNAMO
Language Computer Corporation explores the automatic identification and characterization of emerging events as reported in textual information feeds, such as news reports.

Complex events of the sort of interest to the IC are made up of multiple component events and contextual influences which over time can converge into an action or event of interest. By identifying component events and tracking them over time, event structures can emerge, supporting indicators and warnings to analysts as an input to their task prioritization process.

Performer: Language Computer Corporation

Resulting Research: W911NF-10-C-0096
Model Drift
The goal of the Model Drift seedling is to develop and validate a computation framework for scalable and efficient model drift detection, characterization and correction in unsupervised settings.

"Model Drift", for this seedling, refers to the discrepancy in machine learning environments between the training (source) and test (target) datasets. Such discrepancies may arise from the gradual or abrupt shift in the characteristics of the input data, or the gradual or abrup invalidation of core assumptions used in the development of the model.

Performer: University of Southern California

Resulting Research: FA8750-15-C-0071

Preventative Monitoring of Infectious Agents (PREMONITION)
The PREMONITION seedling is an effort to develop a computational pipeline for detecting potential pathogens from mosquito body contents and constructing candidate vector-host, vector-pathogen, and host-pathogen interactions

The inputs to this computational pipeline are three correlated data streams collected from autonomous mosquito traps: metagenomic data, wingbeat spectra, and environmental factors. The pipeline is tunable, according to the biological processes that degrade host and pathogen nucleic acids in a time-dependent manner, based on vector conditions. Data from pathogen surveillance programs can lead to better epidemiological models and give health organizations more time to optimally prepare responses.

Performer: Vanderbilt University

Resulting Research: D15PC00304

Reading Between the Lines
Lymba Corporation explores the automatic identification and characterization of emerging events as reported in textual information feeds, such as news reports.

Complex events of the sort of interest to the IC are made up of multiple component events and contextual influences which over time can converge into an action or event of interest. By identifying component events and tracking them over time, event structures can emerge, supporting indicators and warnings to analysts as an input to their task prioritization process.

Performer: Lymba Corporation

Resulting Research: W911NF-10-C-0101
Reliable Inference with Missing, Masked, Malfunctioning, or Malicious Sensors
Reliable Inference with Missing, Masked, Malfunctioning or Malicious Sensors (RIM4S) is developing a unified approach to obtain reliable inferences from sensor networks when data reported may have missing, masked, or inaccurate data.

These errors may be unintentional (e.g., malfunctioning sensors) or intentional (e.g., maliciously tampered with, or deleted or obscured for privacy preservation). RIM4S represents multiple error types using a probabilistic graphical model, and then uses belief propagation to efficiently evaluate candidate hypotheses. Current methods to compensate for these types of errors are to threshold and reject data or sensors believed to be inaccurate or unreliable. The proposed approach promises to be able to use degraded information, enabling inference with fewer sensors or measurements.

Performer: University of California, Los Angeles

Resulting Research: N66001-12-C-2014
Text-Driven Forecasting
Carnegie Mellon University explores the potential of purely statistical models that estimate the future influence of scientific publications from the textual content of those publications.

If successful, the research could lay the groundwork for a larger program to generate predictive models from a wide range of document types (e.g., news articles, speeches, financial reports).

Performer: Carnegie Mellon University

Resulting Research: N10PC20222

Collection

NameDescription
Comb-based Multidimensional Coherent Spectroscopy (CBMCS)
Multidimensional spectroscopy is an established technique in the radiofrequency (RF) regime, providing a solution to the problem of spectral clutter and overlapping peaks.

However, multidimensional spectroscopy requires control and measurement of the phases of the electromagnetic waveforms, which while relatively straightforward to accomplish for RF is much more difficult in the optical domain. Demonstrations to date of optical multidimensional spectroscopy require extremely precise optical delay lines and are not suitable for field deployment. Separately, dual comb spectroscopy has developed as a new approach to Fourier Transform optical spectroscopy, without the need for a scanning mechanical delay line. CBMCS seeks to demonstrate multidimensional coherent spectroscopy using three optical frequency combs. The approach under development in the seedling, if successful, could pave the way for development of fieldable devices for chemical detection with high clutter rejection, covert optical communications, and improved synthetic aperture Laser Detection and Ranging (LADAR) waveforms.

Performer: University of Michigan

Resulting Research: 2016-16041300005

Dual Frequency Optical Comb (DFOC)
The Dual Frequency Optical Comb seedling seeks to demonstrate the key advances needed to build a dual comb spectrometer using a single fiber laser cavity that contains two independent, orthogonal polarization states.

Dual comb spectroscopy is an extremely powerful technique for acquiring high sensitivity, broadband, and extremely high resolution optical spectra for chemical identification and quantification. However, despite its sensitivity and resolution advantages, dual comb spectroscopy is challenging for field implementation, since it requires the repetition rate and phase coherence between two independent laser comb sources to be synchronized and stabilized. This typically requires complex electronic feedback loops and additional reference lasers. In the proposed approach, the two pulse trains constituting the two combs are generated from a common cavity within a passively mode-locked fiber laser. Therefore phase noise from relative intensity noise of the pump laser and the frequency variation due to environmental effects will be predominantly common-mode, enabling phase synchronization between the two optical combs without a complicated and expensive feedback system.

Performer: University of Kansas

Resulting Research: 2017-17030700017

Electrically Small Antenna Development
The Electrically Small Antenna Development seedling is developing non-Foster broadband impedance matching circuits using negative resistors.

Current non-Foster matching networks use negative capacitors and negative inductors, for which it is a significant challenge to maintain linearity and stability. Using negative resistors should provide fewer parasitic effects, lower current and voltage magnitudes, and improve linearity. The project includes analysis, simulation, design, building, and testing of broadband matching networks for receive and transmit antenna matching. The testing will compare the performance without matching networks and with active matching circuits.

Performer: Northrop Grumman Corporation

Resulting Research: N66001-12-C-2015
Optically-pumped, cavity-enhanced organic small molecule lasers on-chip (OPC)
The OPC seedling seeks to create an organic, optically-pumped solid state laser integrated onto a silicon wafer.

The proposal consists of three main components: 1) synthesis and fabrication of three nonlinear optical (NLO) small organic molecules, 2) grafting those NLO molecules onto fabricated silicon microcavities, and 3) characterization of the grafted NLO microcavity’s performance as a low-threshold microlaser. The central advance of this proposal is the grafting technique by which nonlinear optical small organic molecules can be grafted onto a silicon nano-structure.

Performer: University of Southern California

Resulting Research: 2016-16070100002

Predicting Craniofacial Morphology (PCM)
The focus of the seedling is to investigate the potential for predictive associations from two different genomic datasets:

1) genes associated with craniofacial development, and 2) CpG lock methylation patterns associated with age estimation.

Performer: Bode Technology Group, Inc.

Resulting Research: D15PC00002

Silicon Microresonator-Based Frequency Combs for High-Performance Spectroscopic Devices in the Mid-Infrared
The goal of the Silicon Microresonator-Based Frequency Combs for High-Performance Spectroscopic Devices in the Mid-Infrared (SMFC) seedling is to investigate and develop a compact, integrated mid-infrared (MIR) frequency comb source in the 4-8 micron spectral region based on MIR parametric comb generation in microresonators.

A frequency comb is a coherent light source whose spectrum consists of a series of discrete, equally spaced elements. The SMFC seedling is investigating air-clad, etchless silicon microresonators for comb generation in the MIR. It is also developing fabrication techniques to reduce coupling and propagation losses and optimizing the microresonator dispersion to enable generation of ultra-broadband combs. Potential applications enabled by the SMFC technology include high-resolution spectroscopy, high-speed detection and ultrashort pulse generation.

Performer: Cornell University

Resulting Research: FA8650-15-C-9100
Spherically Curved III-V Infrared Sensors (SCIS)
Across the spectrum, curved focal planes allow lenses to be designed with fewer elements and faster apertures, improving cost, size, weight, and power.

Curved focal planes also have the potential to increase image sharpness and increase edge illumination compared to flat field optics. Advancements in the ability to create highly curved infra-red (IR) imaging sensors can enable compact, high-performance, wide field-of-view (FOV) optical systems for low SWaP intelligence, surveillance, and reconnaissance (ISR) applications. HRL has previously demonstrated a unique free- edge compressive bending technique with lithographically thinned commercial off the shelf (COTS) silicon complementary metal oxide semiconductor (CMOS) imaging sensors to create working devices with spherical surfaces exceeding 25° of subtended angle, which is 3X greater than the current state-of-the-art for curved visible-region imaging sensors. The SCIS seedling effort seeks to extend both the compressive bending and patterned strain relief approaches to group III-V Short Wave Infrared (SWIR) and Mid Wave Infrared (MWIR) sensors (including InGaAs, InAs/GaSb and InAsSb).

Performer: Hughes Research Laboratories, Llc.

Resulting Research: 2017-17033000004

Smart Devices Technical Study
The study will report on forecasts of smart device technologies, characterization and overview of technical domains associated with networks of Smart Devices, current state-of-the-art, and intended operational use by developers and end-users, and opportunities and challenges for the IC.

 

Performer: Mitre Corporation

Resulting Research: W911NF-13-C-0085
Micro Collector Injector (uCOIN)
The uCOIN seedling’s goal is to passively collect environmental gasses, and then provide concentrated injection of the sample into a micro-gas chromatograph/mass spectrometer system.

The first stage of the device is a radial sampler with two concentric absorbent bed regions. The “upstream” absorbent bed region is a lower surface area bed which has high capacity and high desorption efficiency for semi-volatiles, while the “downstream” absorbent bed is a higher surface bed, which is better suited for trapping and desorbing more volatile compounds. The combination of these sorbent beds provides efficient absorption across a wide spectrum of sample types and concentrations. After sample collection is complete, thermal desorption liberates the collected analyte from the first stage of the device in 2-5 seconds and “dumps” this desorbed gas into the second stage of the device. In the second stage, progressive heating “rolls up” the vapor sample into a tight bunch, which is then released into the separator. The two stage device is projected to provide injection of the concentrated gas aliquot in ~250 ms, with a conservative projected power requirement per cycle of only 65 J. The device also provides a capability for split injections to further compress the temporal pulse of the emitted gas, and the ability to vent water vapor prior to injection. Room temperature ionic liquids (RTILs) will be tested as an alternative sorbent material, providing the potential for a unique new class of sorbents.

Performer: University of Michigan

Resulting Research: 2017-17012600004

Ultraviolet-Visible Photonic Integrated Circuits (UVPC)
Indium phosphide based photonic integrated circuits operating in the “red” visible to near infrared (850-1650 nm) wavelength region are key enablers for modern monolithic tunable lasers, widely tunable lasers, externally modulated lasers and transmitters, and integrated receivers.

Extending active photonic integrated circuit technology to the ultraviolet and “blue” visible range would enable a much wider range of applications, including ion-trap quantum computing, quantum photonics, visible light communications/interconnects, imaging for forgery analysis, on-chip photoionization, and extremely compact, low power UV Raman spectroscopy (via chip based contact spectroscopy or phased array stand-off sensing). The UV-PC seedling effort will demonstrate the key fundamental components of a UV-visible photonic integrated circuit platform, including waveguides, resonators, grating couplers, electro-optic modulators, phase shifters, Mach-Zehnder switches, and splitters.

Performer: Raytheon BBN Technologies

Resulting Research: 2017-17063000002

Computing

NameDescription
Analog CMOS
Transmutational materials change over time, causing a related change in bulk material properties such as electrical and thermal conductance or mass density.

Where such processes are reliable and unaffected by physical effects such as electromagnetic and mechanical forces and temperature, the change in bulk properties can be utilized as a reliable measure of time or as a chip attribution and identification scheme. Used with integrated circuits, such materials have potential applications for: autonomous, self-timed fuse, chip attribution and identification, and low-power time measurement. This seedling identified and studied suitable transmutational materials, developed CMOS-compatible processes for use in integrated circuits, and identified, characterized, and demonstrated potential applications.

Performer: NanoMason

Resulting Research: N66001-C-13-2004

Bloom Filters
The Bloom Filters seedling aims to use very high speed energy-efficient superconductor circuits and Reciprocal Quantum Logic (RQL) in the cryogenic region to perform the Bloom filter functions.

The goal of this seedling is to develop the general architecture of the RQL Bloom filter circuit family and the cell-level designs of these circuits. The energy efficiency of these designs will be calculated by collecting (through simulation) energy consumption data for Signature Insertion (write) and Query (read &check) operations for different lengths of data streams and different numbers of hash functions per stream.

Performer: Stony Brook University

Resulting Research: 2017-16113000003

Chakra High Performance Computing (HPC)
The purpose of the seedling is to research computing architectures using analytical, simulation-based, and micro-architecture design techniques.

For this seedling, software simulations were used to quantify the benefits of a general purpose computer architecture (referred to as "Chakra" - the name for the overall design of this High Performance Computing (HPC) system) to study the reference memory behavior.

Performer: APIC Corporation

Resulting Research: D15PC00202

Environmental Characterization and Response (ECR)
Environmental Characterization and Response (ECR) examines the applicability of utilizing side-channels to characterize the virtualizing environment from the perspective of the virtualized guest.

ECR aims to allow for a less-privileged guest instance to monitor the behavior of the hypervisor to detect changes or suspicious behavior.  ECR consists of three core tasks: (1) test framework development (2) development and evaluation of a side-channel sensor and (3) final documentation.

Performer: Assured Information Security

Resulting Research: 2017-16120700002

Memristor Processors
Hewlett-Packard Laboratories (HP) has proposed to demonstrate unconventional hybrid computation engines employing memristor crossbars.

These so-called dot-product-engines are estimated to enhance speed and power efficiency by orders of magnitude, for many algorithms important to the IC. These algorithms rely on matrix and vector operations, and include but are not limited to pattern recognition in image and video analysis, Fast Fourier Transform, Monte Carlo sampling and optimization, polynomial convolution, streaming data classification, and matrix rank calculation. The goal of this IARPA seedling is to produce specialized, high-power performance analog and digital hardware for performing real vector-matrix multiplication. As is usually the case with analog devices, precision is a key challenge. This effort does not intend to produce devices that perform high-precision computation. Hence the utility of proposed hardware is limited to algorithms where exactness of the computation is not the primary concern.

Performer: Hewlett Packard

Resulting Research: 2014-14080800008
Photon-Photon Gate
The Photon-Photon Gate seedling researches high speed, optical switching as one approaches the quantum-mechanical limit.

In principle, a photon-photon gate offers the ability to switch a data bit at high operating speeds with minimal expended energy. The University of California, San Diego research team is seeking to create an optical parametric mixer that exhibits near-isotropic photon gate response. This mixer will be constructed by measuring the fluctuations along the length of an optical fiber with sub-nanometer accuracy and using this information to synthesize a waveguide with near-ideal response. The seedling project culminates with the demonstration of an all-optical gate that is capable of single-bit switching with fewer than 20 photons at a rate of 100Gbps.

Performer: University of California, San Diego

Resulting Research: 2013-13070200004
RApid Machine-learning Processing Applications and Reconfigurable Targeting of Security (RAMPARTS)
The goal of the RAMPARTS seedling is to develop preliminary capabilities in the area of Fully Homomorphic Encryption (FHE) computing.

Through specific use cases, RAMPARTS will assess apsects of the practical ease of programming, automation of compiling, and effective optimization of FHE computations.

Performer: Galois, Inc.

Resulting Research: 2016-16070700001

Single-flux Quantum Logic Circuits (SQLC)
The goal of the seedling is to investigate the state-of-the-art design and optimization of single-flux quantum (SFQ) logic circuits and come up with a comprehensive research plan for developing a standard design methodology and supporting computer-aided design tools for SFQ logic at the register-transfer-level and below.

The research included using SFQ devices for classical computing and using quantum effects to switch signals.

Performer: University of Southern California

Resulting Research: FA8750-15-C-0203

Spectral Holographic Optical Processing
The S2 Corporation technology employs spatial-spectral holograms in cryogenically cooled crystals, and provides an all-optical means of implementing a cross-correlation algorithm to search for patterns of interest in high-speed (> 100 GHz) optical data.

Efforts in the first year served to identify limitations of single, linear hologram templates working with serial streaming data. This led to the proposal of angular multiplexing to massively scale the technology to search for millions of sub-patterns of interest on >100 GHz data. By redesigning the S2 system to employ spatial + angular multiplexing, together with novel template superposition, it is anticipated the number of programmable sub-patterns can be increased upwards of 1000-fold with 10-100x improvement in power usage per physical channel. By month 15, S2 demonstrated multiplexing of streaming data across 3 angles, with substantial reduction of noise floor and related false positive and negative rates.

Performer: S2 Corporation

Resulting Research: 2014-14020500006
Study: High-Speed Massive Dynamic Graph Analysis
Three studies: Very Large Graphs (VLG2), VLG2, and VLG-Hardware.

VLG1 effort focused on a proof-of-concept demonstration implementing time-dependent graph theory and methods for analyzing very large scale graphs that include temporal dependencies. VLG2 extended the research by studying the impact of uncertainty mechanisms in data on anomalous subgraph detection and to understand and characterize uncertainty in real datasets. VLG-H investigated the advanced hardware system components for processing massive network data two to four orders of magnitude beyond existing capabilities. Specific technical improvements to be explored are data dependent optimization, a high bandwidth communication network, cache-less memory, specialized processing for sparse matrices, low-power circuitry, a sparse array instruction set, and semantic processing.

Performer: Massachusetts Institute of Technology, Lincoln Laboratory

Resulting Research: FA8721-05-C-002