What is SWaP-Constrained Embedded Computing for Artificial Intelligence?
The U.S Air Force and Artificial Intelligence Hardware
We can of course seriously question whether advances in the field of artificial intelligence should be used for military purposes, I truly believe very few would want this to happen. Still there is development of technology in the military and this article is exploring a current venue of research posed in one media outlet online. This is a very short article touching on one topic within the field of artificial intelligence: SWaP AI.
Reading an article in Military and Aerospace Electronics I came across one about SWaP-constrained embedded computing for artificial intelligence.
What is SWaP AI solutions?
SWaP AI: low size, weight, and power (SWaP) artificial intelligence (AI) solutions.
The Air Force in the U.S has stated it wants small, lightweight embedded computing for artificial intelligence (AI) and machine learning (AI/ML) capabilities in an embedded computing environment.
It seems there is a larger research project ($99 million) outlined seeking to achieve orders of magnitude improvement in size, weight and power (SWaP) for deploying artificial intelligence and machine learning capabilities in an embedded computing environment.
U.S. Air Force researchers asking for industry help.
“Officials of the Air Force Research Laboratory’s Information Directorate in Rome, N.Y., issued a broad agency announcement on Thursday (FA875019S7007) for the Robust and Efficient Computing Architectures, Algorithms, and Applications For Embedded Deep Learning.”
As such there is an outline out there.
- Improved autonomy.
- Intelligence, and assurance for command.
- Control.
- Communications.
“The Air Force needs unconventional computing architectures for pattern recognition, event reasoning, decision making, adaptive learning, and autonomous tasking on energy-efficient Air Force manned and unmanned aircraft.”
One interest aspect may be the major focus area. Neuromorphic computing — or brain-inspired computing.
It may involve emerging nanotechnology like memristors and nano-photonics, researchers say.
“Memristors are used in digital memory, logic circuits, biological and neuromorphic systems. Memristors are used in neural networks as well as analog electronics. Remote sensing & Low-power applications. They have their own ability for storing analog and digital data in an easy as well as power efficient method.”
“Nanophotonics or nano-optics is the study of the behavior of light on the nanometer scale, and of the interaction of nanometer-scale objects with light.”
There is a lot of development of hardware related to AI.
If there were to be advances in the field of artificial intelligence within this area it may seem niche, but could have other kind of applications.
There is talk of modular designs.
In this sense to support interchangeable sensors, with automatic software reconfiguration based on available resources.
Companies participating should:
- “Choose computing and interfaces to accommodate expected growth in data bandwidth in future systems. SWaP optimization is a priority.
- Other considerations are ways to validate and improve scalability and security for artificial intelligence and machine learning technologies; deep-learning models and algorithms; and power-aware and energy-optimized deep learning models and algorithms for embedded computing.”
DARP was apparently asking for this a year ago.
It will be interesting to see whether these solutions will be pursued elsewhere outside of the defence industry.
This is #500daysofAI and you are reading article 329. I am writing one new article about or related to artificial intelligence every day for 500 days. My focus for day 300–400 is about AI, hardware and the climate crisis.
The potential $99 million five-year project seeks to achieve improvements in SWaP for artificial intelligence and machine learning embedded computing.
