The Internet of Things (IoT) is an interface with the physical world that usually operates in
random-sparse-event (RSE) scenarios. This article discusses main challenges of IoT chips …

Modern computation based on von Neumann architecture is now a mature cutting-edge
science. In the von Neumann architecture, processing and memory units are implemented …

Processing-in-memory (PIM) is a promising architecture to design various types of neural
network accelerators as it ensures the efficiency of computation together with Resistive …

Recent work demonstrated the promise of using resistive random access memory (ReRAM)
as an emerging technology to perform inherently parallel analog domain in-situ matrix …

Processing-In-Memory (PIM) accelerators have the potential to efficiently run Deep Neural
Network (DNN) inference by reducing costly data movement and by using resistive RAM …

Graph convolutional networks (GCNs) are promising to enable machine learning on graphs.
GCNs exhibit mixed computational kernels, involving regular neural-network-like computing …

We introduce a highly heterogeneous and programmable compute-in-memory (CIM)
accelerator architecture for deep neural network (DNN) inference. This architecture …

As its core computation, a self-attention mechanism gauges pairwise correlations across the
entire input sequence. Despite favorable performance, calculating pairwise correlations is …

Conventional von Neumann architecture faces many challenges in dealing with data-
intensive artificial intelligence tasks efficiently due to huge amounts of data movement …

Recent research demonstrated the promise of using resistive random access memory
(ReRAM) as an emerging technology to perform inherently parallel analog domain in-situ …