Defense Advanced Research Projects AgencyTagged Content List

Information or Signal Processing

Computational tools and techiques for manipulating, analyzing, and synthesizing signals and data

Showing 55 results for Processing RSS
Today’s network subsystems are a major performance bottleneck on the paths that interconnect multiprocessor servers. In comparison with processing speeds, parallelization, and storage speed-ups, the capacity of network links has relatively worsened over time, and to a dramatic extent. This bottleneck has remained unaddressed due to commercial incentives focused on incremental technology advances across multiple, independent market siloes in network and server technology. This has made network interface cards (NICs), which bridge the network/server boundary, an afterthought in both technology marketplaces.
ITA3 will determine the practical and fundamental limits to imaging using low frequency electromagnetic waves.
Lasers have made a tremendous impact on our world – they are essential to diverse fields such as optical communications, remote sensing, manufacturing, and medicine. At the same time, photonic integrated circuits have allowed unprecedented advances in optical systems for Department of Defense (DoD) applications, including LiDAR, signal processing, chip-scale optical clocks, gyros, and data transmission. However, these two technologies today are limited by the incompatibility of the materials used to create them – silicon photonics are easy to manufacture but are poor light emitters while compound semiconductors enable efficient emitters but are difficult to scale for complex integrated circuits.
LADS will develop a new protection paradigm that separates security-monitoring functionality from the protected system, focusing on low-resource, embedded and Internet of Things (IoT) devices. The program will explore technologies to associate the running state of a device with its involuntary analog emissions across different physical modalities including, but not limited to, electromagnetic emissions, acoustic emanations, power fluctuations and thermal output variations.
The past decade has seen explosive growth in development and training of artificial intelligence (AI) systems. However, as AI has taken on progressively more complex problems, the amount of computation required to train the largest AI systems has been increasing ten-fold annually. While AI advances are beginning to have a deep impact in digital computing processes, trade-offs between computational capability, resources and size, weight, and power consumption (SWaP) will become increasingly critical in the near future.