Two-dimensional fully ferroelectric-gated hybrid computing-in-memory hardware for high-precision and energy-efficient dynamic tracking
Computing in memory (CIM) breaks the conventional von Neumann bottleneck through in
situ processing. Monolithic integration of digital and analog CIM hardware, ensuring both …
situ processing. Monolithic integration of digital and analog CIM hardware, ensuring both …
An in-situ dynamic quantization with 3D stacking synaptic memory for power-aware neuromorphic architecture
Spiking Neural Networks (SNNs) show their potential for lightweight low-power inferences
because they mimic the functionality of the biological brain. However, one of the major …
because they mimic the functionality of the biological brain. However, one of the major …
Trends and Challenges in Computing-in-Memory for Neural Network Model: A Review From Device Design to Application-Side Optimization
Neural network models have been widely used in various fields as the main way to solve
problems in the current artificial intelligence (AI) field. Efficient execution of neural network …
problems in the current artificial intelligence (AI) field. Efficient execution of neural network …
Acceleration of nuclear reactor simulation and uncertainty quantification using low-precision arithmetic
In recent years, interest in approximate computing has been increasing significantly in many
disciplines in the context of saving energy and computation cost by trading off on the quality …
disciplines in the context of saving energy and computation cost by trading off on the quality …
HuNT: Exploiting Heterogeneous PIM Devices to Design a 3-D Manycore Architecture for DNN Training
Processing-in-memory (PIM) architectures have emerged as an attractive computing
paradigm for accelerating deep neural network (DNN) training and inferencing. However, a …
paradigm for accelerating deep neural network (DNN) training and inferencing. However, a …
Power-Aware Neuromorphic Architecture With Partial Voltage Scaling 3-D Stacking Synaptic Memory
The combination of neuromorphic computing (NC) and 3-D integrated circuits-the 3-D
stacking neuromorphic system can be the most advanced architecture that inherits the …
stacking neuromorphic system can be the most advanced architecture that inherits the …
Pacim: A sparsity-centric hybrid compute-in-memory architecture via probabilistic approximation
Approximate computing emerges as a promising approach to enhance the efficiency of
compute-in-memory (CiM) systems in deep neural network processing. However, traditional …
compute-in-memory (CiM) systems in deep neural network processing. However, traditional …
Approx-IMC: A general-purpose approximate digital in-memory computing framework based on STT-MRAM
In-memory computing (IMC) empowers von Neumann-based computing systems to meet the
performance and energy requirements of emerging data-intensive applications by offloading …
performance and energy requirements of emerging data-intensive applications by offloading …
HyDe: A Hybrid PCM/FeFET/SRAM Device-search for Optimizing Area and Energy-efficiencies in Analog IMC Platforms
Today, there are a plethora of In-Memory Computing (IMC) devices-SRAMs, PCMs &
FeFETs, that emulate convolutions on crossbar-arrays with high throughput. Each IMC …
FeFETs, that emulate convolutions on crossbar-arrays with high throughput. Each IMC …
LOGIC: Logic Synthesis for Digital In-Memory Computing
In-memory processing offers a promising solution for enhancing the performance of data-
intensive applications. While analog in-memory computing demonstrates remarkable …
intensive applications. While analog in-memory computing demonstrates remarkable …