Defense Advanced Research Projects AgencyTagged Content List


A process or rule set used for calculations or other problem-solving operations

Showing 13 results for Algorithms + Programs RSS
Machine learning has shown remarkable success across many application areas in recent years, leveraging advances in computing power and the availability of large sets of training data. It provides a tremendous opportunity to deploy data-driven systems in more complex and interactive tasks including personalized autonomy, agile robotics, self-driving vehicles, and smart cities. Despite dramatic progress, the machine learning community still lacks an understanding of the trade-offs and mathematical limitations of related technologies for a given domain, problem, or dataset.
The ultimate goal of the DARPA Accelerated Computation for Efficient Scientific Simulation (ACCESS) is to demonstrate new, specialized benchtop technology that can solve large problems in complex physical systems on the hour timescale, compared to existing methods that require full cluster-scale supercomputing resources and take weeks to months. The core principle of the program is to leverage advances in optics, MEMS, additive manufacturing, and other emerging technologies to develop new non-traditional hybrid analog and digital computational means.
The A-to-I Look-Through Program will fundamentally improve the operational bandwidth, linearity, and efficiency of electronic systems where the objective is to receive and transmit information using electromagnetic (radio) waves under extreme size/weight/power and environmental conditions required for DoD applications. This will be achieved by developing new electronic processing subsystems methods and architectures based on new understandings of mathematical principles and embedded signal processing. This program will develop ultra-wideband digital RF receivers based on A-to-I converter (AIC) technology.
Deep Purple aims to advance the modeling of complex dynamic systems using new information-efficient approaches that make optimal use of data and known physics at multiple scales. The program is investigating next-generation deep learning approaches that use not only high throughput multimodal scientific data from observations and controlled experiments (including behaviors such as phase transitions and chaos), but also of the known science of such systems at whatever scales it exists.
Conventional analog-to-digital converters (ADCs) are fundamentally limited by timing jitter in the sampling source, forcing a trade-off between bandwidth and resolution. As a result, radio frequency (RF) systems are typically designed with narrow-bandwidth channels. These engineering constraints present problems when faced with broadband signals and ultra-short pulses. At high carrier frequencies, RF systems are further limited by the tuner that must mix down to baseband for electronic digitization.