115 citations · 463 across the 11 of their papers we have counts for
11 papers
Tunable Synaptic Working Memory with Volatile Memristive Devices
Saverio Ricci, David Kappel, Christian Tetzlaff +2
Different real-world cognitive tasks evolve on different relevant timescales. Processing these tasks requires memory mechanisms able to match their specific time constants. In part…
A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platforms
Cristina Silvano, Daniele Ielmini, Fabrizio Ferrandi +19
Recent trends in deep learning (DL) have made hardware accelerators essential for various high-performance computing (HPC) applications, including image classification, computer vi…
Binary stochasticity enabled highly efficient neuromorphic deep learning achieves better-than-software accuracy
Yang Li, Wei Wang, Ming Wang +13
Deep learning needs high-precision handling of forwarding signals, backpropagating errors, and updating weights. This is inherently required by the learning algorithm since the gra…
Decision Making by a Neuromorphic Network of Volatile Resistive Switching Memories
Saverio Ricci, David Kappel, Christian Tetzlaff +2
The necessity of having an electronic device working in relevant biological time scales with a small footprint boosted the research of a new class of emerging memories. Ag-based vo…
Tutorial: Analog Matrix Computing (AMC) with Crosspoint Resistive Memory Arrays
Zhong Sun, Daniele Ielmini
Matrix computation is ubiquitous in modern scientific and engineering fields. Due to the high computational complexity in conventional digital computers, matrix computation represe…
2022 Roadmap on Neuromorphic Computing and Engineering
Dennis V. Christensen, Regina Dittmann, Bernabé Linares-Barranco +56
Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as sep…