The future of ferroelectric field-effect transistor technology
The discovery of ferroelectricity in oxides that are compatible with modern semiconductor
manufacturing processes, such as hafnium oxide, has led to a re-emergence of the …
manufacturing processes, such as hafnium oxide, has led to a re-emergence of the …
Neuromorphic devices based on fluorite‐structured ferroelectrics
A continuous exponential rise has been observed in the storage and processing of the data
that may not curtail in the foreseeable future. The required data processing speed and …
that may not curtail in the foreseeable future. The required data processing speed and …
DNN+ NeuroSim V2. 0: An end-to-end benchmarking framework for compute-in-memory accelerators for on-chip training
DNN+ NeuroSim is an integrated framework to benchmark compute-in-memory (CIM)
accelerators for deep neural networks, with hierarchical design options from device-level, to …
accelerators for deep neural networks, with hierarchical design options from device-level, to …
Ferroelectric FETs With 20-nm-Thick HfO2 Layer for Large Memory Window and High Performance
H Mulaosmanovic, ET Breyer… - … on Electron Devices, 2019 - ieeexplore.ieee.org
Hafnium oxide (HfO 2)-based ferroelectric field-effect transistor (FeFET) is an attractive
device for nonvolatile memory. However, when compared to the well-established flash …
device for nonvolatile memory. However, when compared to the well-established flash …
Supervised learning in all FeFET-based spiking neural network: Opportunities and challenges
The two possible pathways toward artificial intelligence (AI)—(i) neuroscience-oriented
neuromorphic computing [like spiking neural network (SNN)] and (ii) computer science …
neuromorphic computing [like spiking neural network (SNN)] and (ii) computer science …
NeuroSim simulator for compute-in-memory hardware accelerator: Validation and benchmark
Compute-in-memory (CIM) is an attractive solution to process the extensive workloads of
multiply-and-accumulate (MAC) operations in deep neural network (DNN) hardware …
multiply-and-accumulate (MAC) operations in deep neural network (DNN) hardware …
Edge intelligence—On the challenging road to a trillion smart connected IoT devices
A Keshavarzi, W van den Hoek - IEEE Design & Test, 2019 - ieeexplore.ieee.org
Editor's note: This article provides an industry and government perspective to the problem of
intelligence resource constrained IoT nodes and presents the vision of the future and …
intelligence resource constrained IoT nodes and presents the vision of the future and …
Two-step write–verify scheme and impact of the read noise in multilevel RRAM-based inference engine
Accurate cell conductance tuning is critical to realizing multilevel resistive random access
memory (RRAM)-based compute-in-memory inference engines. To tighten the distribution of …
memory (RRAM)-based compute-in-memory inference engines. To tighten the distribution of …
In-memory computing to break the memory wall
Facing the computing demands of Internet of things (IoT) and artificial intelligence (AI), the
cost induced by moving the data between the central processing unit (CPU) and memory is …
cost induced by moving the data between the central processing unit (CPU) and memory is …
Analog-to-digital converter design exploration for compute-in-memory accelerators
This article comprehensively investigates analog-to-digital converter (ADC) design for
compute-in-memory array. The authors show that 6-bit ADC precision is sufficient to …
compute-in-memory array. The authors show that 6-bit ADC precision is sufficient to …