Defense Advanced Research Projects AgencyTagged Content List

Algorithms

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

Showing 32 results for Algorithms + Programs RSS
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.
The general-purpose computer has remained the dominant computing architecture for the last 50 years, driven largely by the relentless pace of Moore’s Law. As this trajectory shows signs of slowing, however, it has become increasingly more challenging to achieve performance gains from generalized hardware, setting the stage for a resurgence in specialized architectures. Today’s specialized, application-specific integrated circuits (ASICs) — hardware customized for a specific application — offer limited flexibility and are costly to design, fabricate, and program.
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.
FunCC aims to uncover fundamental principles of resilient self-organized complex systems applicable to domains spanning autonomous systems to biological networks, the immune system, and ecosystems. The dynamics and evolution of complex collectives are explored using new frameworks that embrace agent heterogeneity, stochasticity, distributed control, and diffusion of (mis)information.