Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL)
techniques, which rely on networks of connected simple computing units operating in …

In this article, we review the existing analog resistive switching memory (RSM) devices and
their hardware technologies for in-memory learning, as well as their challenges and …

As data movement operations and power-budget become key bottlenecks in the design of
computing systems, the interest in unconventional approaches such as processing-in …

Traditional computing systems based on the von Neumann architecture are fundamentally
bottlenecked by data transfers between processors and memory. The emergence of data …

Inspired by biology, neuromorphic systems have been trying to emulate the human brain for
decades, taking advantage of its massive parallelism and sparse information coding …

With the in-memory processing ability, ReRAM based computing gets more and more
attractive for accelerating neural networks (NNs). However, most ReRAM based …

Today's high performance computing (HPC) systems are limited by the expensive data
movement between processing and memory units. An emerging solution strategy is to …

Big-data storage poses significant challenges to anonymization of sensitive information
against data sniffing. Not only will the encryption bandwidth be limited by the I/O traffic, the …

The recently emerging resistive random-access memory (RRAM) can provide nonvolatile
memory storage but also intrinsic computing for matrix-vector multiplication, which is ideal …

The processing in-memory (PIM) approach that combines memory and processor appears to
solve the memory wall problem. NAND flash memory, which is widely adopted in edge …