2020 BRN Discussion, page-8683

relevant extract from the article:

5) AI at the edge – Edge devices / on-premise devices play a large role in IoT systems. In the context of AI based
analytics, they play significant role to provide
a. Low-latency, real-time inferencing needed for IoT-driven process control systems.
b. Low-battery consumption that is needed for energy-constrained devices like wearable and implantable.
c. Privacy-preserving analytics as the data does not leave the edge / premises even for analytics.
AI at edge either needs special technique to compress the AI models enabling them to run on constrained edge devices,
or have dedicated low-latency, low memory inferencing algorithms, or have special purpose hardware accelerators in
the edge [25], [26]. In order to reduce the energy consumption by significant order, completely new processor
architectures called neuromorphic [27], that mimic the brain in hardware, are been used to design new chips - such
chips, coupled with new sensing techniques called spiking sensors and 3rd generation brain-inspired neural networks
called spiking neural networks (SNN) [28] hold the promise of disrupting the low-power edge AI technology.