Memristive devices, which combine a resistor with memory functions such that voltage
pulses can change their resistance (and hence their memory state) in a nonvolatile manner …

As the limits of transistor technology are approached, feature size in integrated circuit
transistors has been reduced very near to the minimum physically-realizable channel length …

Abstract Models of artificial intelligence (AI) that have billions of parameters can achieve
high accuracy across a range of tasks,, but they exacerbate the poor energy efficiency of …

Artificial intelligence edge devices should offer high inference accuracy and rapid response
times, as well as being energy efficient. Ensuring the security of these devices against …

We present a 256 256 in-memory compute (IMC) core designed and fabricated in 14-nm
CMOS technology with backend-integrated multi-level phase change memory (PCM). It …

In this paper, we propose BioHD, a novel genomic sequence searching platform based on
Hyper-Dimensional Computing (HDC) for hardware-friendly computation. BioHD transforms …

Battery-powered edge-AI devices require nonvolatile computing-in-memory (nvCIM) macros
for nonvolatile data storage and multiply-and-accumulate (MAC) operations. High inference …

Efficient edge computing, with sufficiently large on-chip memory capacity, is essential in the
internet-of-everything era. Nonvolatile computing-in-memory (nvCIM) reduces the data …

Artificial intelligence (AI) and machine learning (ML) are revolutionizing many fields of study,
such as visual recognition, natural language processing, autonomous vehicles, and …

Nonvolatile computing-in-memory (nvCIM)[1]–[4] is ideal for battery-powered tiny artificial
intelligence (AI) edge devices that require nonvolatile data storage and low system-level …