Memristive technologies for data storage, computation, encryption, and radio-frequency communication
Memristive devices, which combine a resistor with memory functions such that voltage
pulses can change their resistance (and hence their memory state) in a nonvolatile manner …
pulses can change their resistance (and hence their memory state) in a nonvolatile manner …
Hardware implementation of memristor-based artificial neural networks
Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL)
techniques, which rely on networks of connected simple computing units operating in …
techniques, which rely on networks of connected simple computing units operating in …
[HTML][HTML] A compute-in-memory chip based on resistive random-access memory
Realizing increasingly complex artificial intelligence (AI) functionalities directly on edge
devices calls for unprecedented energy efficiency of edge hardware. Compute-in-memory …
devices calls for unprecedented energy efficiency of edge hardware. Compute-in-memory …
Edge learning using a fully integrated neuro-inspired memristor chip
Learning is highly important for edge intelligence devices to adapt to different application
scenes and owners. Current technologies for training neural networks require moving …
scenes and owners. Current technologies for training neural networks require moving …
Deep physical neural networks trained with backpropagation
Deep-learning models have become pervasive tools in science and engineering. However,
their energy requirements now increasingly limit their scalability. Deep-learning …
their energy requirements now increasingly limit their scalability. Deep-learning …
Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators
Analog in-memory computing—a promising approach for energy-efficient acceleration of
deep learning workloads—computes matrix-vector multiplications but only approximately …
deep learning workloads—computes matrix-vector multiplications but only approximately …
A CMOS-integrated spintronic compute-in-memory macro for secure AI edge devices
YC Chiu, WS Khwa, CS Yang, SH Teng, HY Huang… - Nature …, 2023 - nature.com
Artificial intelligence edge devices should offer high inference accuracy and rapid response
times, as well as being energy efficient. Ensuring the security of these devices against …
times, as well as being energy efficient. Ensuring the security of these devices against …
HERMES-Core—A 1.59-TOPS/mm2 PCM on 14-nm CMOS In-Memory Compute Core Using 300-ps/LSB Linearized CCO-Based ADCs
We present a 256 256 in-memory compute (IMC) core designed and fabricated in 14-nm
CMOS technology with backend-integrated multi-level phase change memory (PCM). It …
CMOS technology with backend-integrated multi-level phase change memory (PCM). It …
A 40-nm, 2M-cell, 8b-precision, hybrid SLC-MLC PCM computing-in-memory macro with 20.5-65.0 TOPS/W for tiny-Al edge devices
Efficient edge computing, with sufficiently large on-chip memory capacity, is essential in the
internet-of-everything era. Nonvolatile computing-in-memory (nvCIM) reduces the data …
internet-of-everything era. Nonvolatile computing-in-memory (nvCIM) reduces the data …
[HTML][HTML] Survey of deep learning accelerators for edge and emerging computing
The unprecedented progress in artificial intelligence (AI), particularly in deep learning
algorithms with ubiquitous internet connected smart devices, has created a high demand for …
algorithms with ubiquitous internet connected smart devices, has created a high demand for …