Learning is highly important for edge intelligence devices to adapt to different application
scenes and owners. Current technologies for training neural networks require moving …

Package-level integration using multi-chip-modules (MCMs) is a promising approach for
building large-scale systems. Compared to a large monolithic die, an MCM combines many …

Computing systems are widely used in medical diagnosis, climate prediction, autonomous
vehicles, etc. As the key part of electronics, the performance of computing systems is crucial …

Recent studies from several hyperscalars pinpoint to embedding layers as the most memory-
intensive deep learning (DL) algorithm being deployed in today's datacenters. This paper …

Latency is of utmost importance in safety-critical systems. In neural networks, lowest
theoretical latency is dependent on the depth of the network. This begs the question--is it …

With the rapid development of 5G, artificial intelligence (AI), and high-performance
computing (HPC), there is a huge increase in the data exchanged between the processor …

Recent trends in deep learning (DL) imposed hardware accelerators as the most viable
solution for several classes of high-performance computing (HPC) applications such as …

Moore's Law and Dennard Scaling have guided the semiconductor industry for the past few
decades. Recently, both laws have faced validity challenges as transistor sizes approach …

Personalized recommendations are the backbone machine learning (ML) algorithm that
powers several important application domains (eg, ads, e-commerce, etc) serviced from …

Artificial Intelligence of Things (AIoT) is an emerging area of future Internet of Things (IoT) to
support intelligent IoT applications. In AIoT, intelligent edge computing technologies …