Efficient training of neuromorphic electronics (opens in new tab)

Neuromorphic electronics can provide in-memory computing systems with low power consumption by emulating key principles of the brain. However, their practical capabilities are limited by a number of challenges, including device non-ideality, limited training accuracy and insufficient adaptability. Here we explore the development of training approaches for neuromorphic electronics, including digital, mixed-signal and emerging neuromorphic electronics. We examine the characteristics and advanta...