Latest from Embedded

144516710_Vladimir_Timofeev_Dreamstime
promo__id_144516710__vladimir_timofeev__dreamstime
ID 84308884 © Andy Chisholm - Dreamstime.com
promo_id_84308884__andy_chisholm__dreamstime
Dreamstime_Monsit-Jangariyawong_117103442
dreamstime_monsitjangariyawong_117103442
Tony Vitolo/Electronic Design
promo1920x1080
ID 83317721 © Igor Zakharevich | Dreamstime.com
supplychain_dreamstime_l_83317721
Framestock-Footages_dreamstime_135514255
Robot Framestock Footages Dreamstime L 135514255 61df45f405130

How PCIe Specs Can Help Build Machine-Learning Accelerators (Download)

Jan. 12, 2022

Read this article online.

Machine-learning (ML), especially deep-learning (DL)-based solutions, are penetrating all aspects of our personal and business lives. ML-based solutions can be found in agriculture, media and advertising, healthcare, defense, law, finance, manufacturing, and e-commerce. On a personal basis, ML touches our lives when we read Google news, play music from our Spotify playlists, in our Amazon recommendations, and when we speak to Alexa or Siri.

Due to the wide usage of machine-learning techniques in business and consumer use cases, it’s evident that systems offering high performance with low total cost of operation for ML applications will be quite attractive to customers deploying such applications. Consequently, there’s a rapidly growing market for chips that efficiently process ML workloads.