Defense Advanced Research Projects AgencyTagged Content List


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

Showing 33 results for Algorithms + News RSS
With the official roll out of the Electronics Resurgence Initiative’s latest investments today, DARPA hopes to open new innovation pathways to address impending engineering and economics challenges that, if left unanswered, could challenge what has been a relentless half-century run of progress in microelectronics technology. To maintain healthy forward momentum, the ERI over the next four years will commit hundreds of millions of dollars to nurture research in advanced new materials, circuit design tools, and system architectures. In addition to a half-dozen or so existing DARPA programs, and the largest program in the U.S. that funds basic electronics research at universities, 
DARPA has successfully completed its Anti-Submarine Warfare (ASW) Continuous Trail Unmanned Vessel (ACTUV) program and has officially transferred the technology demonstration vessel, christened Sea Hunter, to the Office of Naval Research (ONR). ONR will continue developing the revolutionary prototype vehicle—the first of what could ultimately become an entirely new class of ocean-going vessel able to traverse thousands of kilometers over open seas for months at a time, without a single crew member aboard—as the Medium Displacement Unmanned Surface Vehicle (MDUSV).
First announced in June 2017, DARPA’s Electronics Resurgence Initiative (ERI) is a multi-year, upwards of $1.5 billion investment in jumpstarting innovation and collaboration across the U.S. electronics community to address an array of long foreseen challenges to Moore’s Law. To kickoff this community-wide effort, DARPA is hosting its first annual ERI Summit from July 23-25 in San Francisco, CA. The three-day event will bring together leading voices from across the electronics community–including Alphabet, Applied Materials, Intel, Synopsys, Cadence, Mentor Graphics, NVIDIA, and IBM–to address challenges and opportunities for the next half century of electronics progress.
Machine learning (ML) systems today learn by example, ingesting tons of data that has been individually labeled by human analysts to generate a desired output. As these systems have progressed, deep neural networks (DNN) have emerged as the state of the art in ML models. DNN are capable of powering tasks like machine translation and speech or object recognition with a much higher degree of accuracy. However, training DNN requires massive amounts of labeled data–typically 109 or 1010 training examples. The process of amassing and labeling this mountain of information is costly and time consuming.
A once highly manual process, circuit design has been transformed by the advent of electronic design automation (EDA) tools and modular design methodologies. Despite continuing advances in automation technologies, the demand for increasingly complex System-on-Chip (SoC) platforms has shown no sign of slowing. Today’s SoCs incorporate billions of transistors with miles of electrical wiring that are integrated within a tiny chip.