Deep Bayesian active learning using in-memory computing hardware. [PDF]
Lin Y +5 more
europepmc +1 more source
The Role of Phase-Change Memory in Edge Computing and Analog In-Memory Computing: An Overview of Recent Research Contributions and Future Challenges. [PDF]
Antolini A +8 more
europepmc +1 more source
Characterization and Programming Algorithm of Phase Change Memory Cells for Analog In-Memory Computing. [PDF]
Antolini A +6 more
europepmc +1 more source
In-memory-computing CNN accelerator employing charge-domain compute
High-dimensional matrix-vector-multiplications (MVM) are the main operations of deep neural networks (DNN). As the size of DNNs increases, data movement becomes a problem and limits their performance. Analog in-memory computing accelerators are one of the most promising solutions to reduce this problem.
openaire +1 more source
Insights of BDAPbI4-Based Flexible Memristor for Artificial Synapses and In-Memory Computing. [PDF]
Patel M +4 more
europepmc +1 more source
Emerging 2D Ferroelectric Devices for In-Sensor and In-Memory Computing. [PDF]
Chen C, Zhou Y, Tong L, Pang Y, Xu J.
europepmc +1 more source
Rapid learning with phase-change memory-based in-memory computing through learning-to-learn. [PDF]
Ortner T +9 more
europepmc +1 more source
Physical unclonable in-memory computing for simultaneous protecting private data and deep learning models. [PDF]
Yue W +16 more
europepmc +1 more source
Heterogeneous integration of 2D memristor arrays and silicon selectors for compute-in-memory hardware in convolutional neural networks [PDF]
Samarth Jain +5 more
openalex +1 more source

