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

Memristor crossbars are circuits capable of performing analog matrix-vector multiplications,
overcoming the fundamental energy efficiency limitations of digital logic. They have been …

Processing-in-memory (PIM) is a promising solution to address the" memory wall"
challenges for future computer systems. Prior proposed PIM architectures put additional …

Neuromorphic computing is henceforth a major research field for both academic and
industrial actors. As opposed to Von Neumann machines, brain-inspired processors aim at …

Historically, memory technologies have been evaluated based on their storage density, cost,
and latencies. Beyond these metrics, the need to enable smarter and intelligent computing …

Resistive memories enable dramatic energy reductions for neural algorithms. We propose a
general purpose neural architecture that can accelerate many different algorithms and …

Processing in memory (PIM) is a concept to enable massively parallel dot products while
kee** one set of operands in memory. PIM is ideal for computationally demanding deep …

Graph Neural Networks (GNNs) have attracted increasing research interest for their
remarkable capability to model graph-structured knowledge. However, GNNs suffer from …

Deep neural networks (DNNs) achieve best-known accuracies in many machine learning
tasks involved in image, voice, and natural language processing and are being used in an …

With the recent reincarnations of neuromorphic computing comes the promise of a new
computing paradigm, with a focus on the design and fabrication of neuromorphic chips. A …