A configurable multi-precision CNN computing framework based on single bit RRAM
Convolutional Neural Networks (CNNs) play a vital role in machine learning. Emerging
resistive random-access memories (RRAMs) and RRAM-based Processing-In-Memory …
resistive random-access memories (RRAMs) and RRAM-based Processing-In-Memory …
Interconnect-aware area and energy optimization for in-memory acceleration of DNNs
State-of-the-art in-memory computing (IMC) architectures employ an array of homogeneous
tiles and severely underutilize processing elements (PEs). In this article, the authors propose …
tiles and severely underutilize processing elements (PEs). In this article, the authors propose …
Inca: Input-stationary dataflow at outside-the-box thinking about deep learning accelerators
This paper first presents an input-stationary (IS) implemented crossbar accelerator (INCA),
supporting inference and training for deep neural networks (DNNs). Processing-in-memory …
supporting inference and training for deep neural networks (DNNs). Processing-in-memory …
Hybrid analog-digital in-memory computing
Today's high performance computing (HPC) systems are limited by the expensive data
movement between processing and memory units. An emerging solution strategy is to …
movement between processing and memory units. An emerging solution strategy is to …
NAS4RRAM: neural network architecture search for inference on RRAM-based accelerators
The RRAM-based accelerators enable fast and energy-efficient inference for neural
networks. However, there are some requirements to deploy neural networks on RRAM …
networks. However, there are some requirements to deploy neural networks on RRAM …
Gibbon: Efficient co-exploration of NN model and processing-in-memory architecture
The memristor-based Processing-In-Memory (PIM) architectures have shown great potential
to boost the computing energy efficiency of Neural Networks (NNs). Existing work …
to boost the computing energy efficiency of Neural Networks (NNs). Existing work …
Quarry: Quantization-based ADC reduction for ReRAM-based deep neural network accelerators
ReRAM (Resistive Random-Access Memory) crossbar arrays have the potential to provide
extremely fast and low-cost DNN (Deep Neural Network) acceleration. However, peripheral …
extremely fast and low-cost DNN (Deep Neural Network) acceleration. However, peripheral …
High-throughput, area-efficient, and variation-tolerant 3-D in-memory compute system for deep convolutional neural networks
Untethered computing using deep convolutional neural networks (DCNNs) at the edge of
IoT with limited resources requires systems that are exceedingly power and area-efficient …
IoT with limited resources requires systems that are exceedingly power and area-efficient …
MAX2: An ReRAM-Based Neural Network Accelerator That Maximizes Data Reuse and Area Utilization
Although recent advances in resistive random access memory (ReRAM)-based accelerator
designs for deep convolutional neural networks (CNNs) offer energy-efficiency …
designs for deep convolutional neural networks (CNNs) offer energy-efficiency …
BRAHMS: Beyond conventional RRAM-based neural network accelerators using hybrid analog memory system
Accelerating convolutional neural networks (CNNs) with resistive random-access memory
(RRAM) based processing-in-memory systems has been recognized as a promising …
(RRAM) based processing-in-memory systems has been recognized as a promising …