Latest from Embedded

ID 217230663 © Christian Offenberg - Dreamstime.com | electronica.de
promo_messe_munich__id_217230663__christian_offenb
ID 169214712 © Petrsvoboda91 | Dreamstime.com
Focus at SSD slot, type M.2 with support for NVMe, on computer motherboard.
ID 321264446 © Studioclever | Dreamstime.com
Generative AI in the data center
Sparkfun, Mikroe, Digilent, Digi International
Peripheral modules with popular form factors
ID 105492920 © Feblacal | Dreamstime.com
CAD modeling
id_9143019__kiosk88__Dreamstime
Circuit board design
Dreamstime_dedmityay_169125577
Autonomous vehicle navigation and positioning
Teledyne FLIR
London Stroller Promo 2 6317b43c23358

Improving Convolutional Neural Networks at the Edge (Download)

Sept. 6, 2022

Read this article online.

The concept of a perception neural network was first described as early as the 1950s. However, it wasn’t until recently that the necessary training data, neural-network frameworks, and the requisite processing power came together to help launch an artificial-intelligence (AI) revolution. Despite the tremendous growth of AI technology, the AI revolution continuously requires new tools and methods to take full advantage of its promise, especially when dealing with imaging data beyond visible wavelengths of the electromagnetic spectrum.

One such data type is thermal imaging, or the ability to capture long-wave infrared (LWIR) data. Thermal is a sub-type of a much larger world of imaging that emerged in the latter half of the 20th century, including LiDAR and radar. 

Comments

To join the conversation, and become an exclusive member of Electronic Design, create an account today!