Latest from Embedded

100269668 © Ronstik | Dreamstime.com
promo_100269668__ronstik__dreamstime
Irina Toloknovskaia, Dreamstime.com
Circuit Diagram51965608 © Irina Toloknovskaia Dreamstime
ID 122748715 - Design © Everythingpossible - Dreamstime.com
Planner manipulating chart
ID 340558606 - Ai © Yulia Gapeenko - Dreamstime.com
promo__id_340558606__ai__yulia_gapeenko__dreamstim
Dreamstime_thekaikoro_137795256
dreamstime_thekaikoro_137795256_promo
ID 317960473 | Ai Communications © Neirfy | Dreamstime.com
digital_dreamstime_l_317960473
96442672 | Abstract © Bluebay2014 | Dreamstime.com
6669f50fe08a6e53e37807de Cloudcomputing Dreamstime L 96442672

Build Edge AI Systems Using eFPGA Technology (Download)

June 12, 2024

Read this article online.

Artificial intelligence (AI) continues to rapidly evolve and impact more industries on a global scale. From autonomous cars to virtual personal assistants, AI has increasingly integrated into our daily lives. In particular, the use of AI at the edge is becoming widespread, as it enables real-time processing of data near the sensor rather than relying on centralized data centers. Such edge execution of AI allows for a reduction in latency, connectivity dependency, energy consumption, and cost.

Indeed, several hardware options are available for processing data in real-time, such as the central processing unit (CPU), graphical processing unit (GPU), field-programmable gate array (FPGA), system-on-chip (SoC) and application-specific integrated circuit (ASIC). Each of these technologies has its own advantages and disadvantages, and the choice depends on the specific requirements of the target application.

Comments

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