Memristor-based binarized spiking neural networks: Challenges and applications
Memristive arrays are a natural fit to implement spiking neural network (SNN) acceleration.
Representing information as digital spiking events can improve noise margins and tolerance …
Representing information as digital spiking events can improve noise margins and tolerance …
An overview of processing-in-memory circuits for artificial intelligence and machine learning
Artificial intelligence (AI) and machine learning (ML) are revolutionizing many fields of study,
such as visual recognition, natural language processing, autonomous vehicles, and …
such as visual recognition, natural language processing, autonomous vehicles, and …
A 40-nm MLC-RRAM compute-in-memory macro with sparsity control, on-chip write-verify, and temperature-independent ADC references
Resistive random access memory (RRAM)-based compute-in-memory (CIM) has shown
great potential for accelerating deep neural network (DNN) inference. However, device …
great potential for accelerating deep neural network (DNN) inference. However, device …
Thermodynamic origin of nonvolatility in resistive memory
Electronic switches based on the migration of high-density point defects, or memristors, are
poised to revolutionize post-digital electronics. Despite significant research, key …
poised to revolutionize post-digital electronics. Despite significant research, key …
On memristors for enabling energy efficient and enhanced cognitive network functions
S Saleh, B Koldehofe - IEEE Access, 2022 - ieeexplore.ieee.org
The high performance requirements of nowadays computer networks are limiting their ability
to support important requirements of the future. Two important properties essential in …
to support important requirements of the future. Two important properties essential in …
Exploring compute-in-memory architecture granularity for structured pruning of neural networks
Compute-in-Memory (CIM) implemented with Resistive-Random-Access-Memory (RRAM)
crossbars is a promising approach for Deep Neural Network (DNN) acceleration. As the …
crossbars is a promising approach for Deep Neural Network (DNN) acceleration. As the …
Flexible oxide thin film transistors, memristors, and their integration
Flexible electronics have seen extensive research over the past years due to their potential
stretchability and adaptability to non‐flat surfaces. They are key to realizing low‐power …
stretchability and adaptability to non‐flat surfaces. They are key to realizing low‐power …
A look-up table-based processing-in-SRAM architecture for energy-efficient search applications
This paper presents an efficient in-memory computing architecture for search and logic
function applications. The proposed design benefits from an SRAM cell, using two single …
function applications. The proposed design benefits from an SRAM cell, using two single …
Rm-ntt: An rram-based compute-in-memory number theoretic transform accelerator
As more cloud computing resources are used for machine learning training and inference
processes, privacy-preserving techniques that protect data from revealing at the cloud …
processes, privacy-preserving techniques that protect data from revealing at the cloud …
Neuromorphic computing: From devices to integrated circuits
V Saxena - Journal of Vacuum Science & Technology B, 2021 - pubs.aip.org
A variety of nonvolatile memory (NVM) devices including the resistive Random Access
Memory (RRAM) are currently being investigated for implementing energy-efficient …
Memory (RRAM) are currently being investigated for implementing energy-efficient …