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 …

The low communication bandwidth between memory and processing units in conventional
von Neumann machines does not support the requirements of emerging applications that …

Processing In-Memory (PIM) has shown a great potential to accelerate inference tasks of
Convolutional Neural Network (CNN). However, existing PIM architectures do not support …

Analog hardware accelerators, which perform computation within a dense memory array,
have the potential to overcome the major bottlenecks faced by digital hardware for data …

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 …

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

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 …

Deep neural networks (DNNs) have become an essential tool in artificial intelligence, with a
wide range of applications such as computer vision, medical diagnosis, security, robotics …

Today's applications generate a large amount of data that need to be processed by learning
algorithms. In practice, the majority of the data are not associated with any labels …

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