Forms: Fine-grained polarized reram-based in-situ computation for mixed-signal dnn accelerator
Recent work demonstrated the promise of using resistive random access memory (ReRAM)
as an emerging technology to perform inherently parallel analog domain in-situ matrix …
as an emerging technology to perform inherently parallel analog domain in-situ matrix …
Improving dnn fault tolerance using weight pruning and differential crossbar mapping for reram-based edge ai
Recent research demonstrated the promise of using resistive random access memory
(ReRAM) as an emerging technology to perform inherently parallel analog domain in-situ …
(ReRAM) as an emerging technology to perform inherently parallel analog domain in-situ …
Redram: A reconfigurable processing-in-dram platform for accelerating bulk bit-wise operations
In this paper, we propose ReDRAM, as a reconfigurable DRAM-based processing-in-
memory (PIM) accelerator, which transforms current DRAM architecture to massively parallel …
memory (PIM) accelerator, which transforms current DRAM architecture to massively parallel …
Admm-nn: An algorithm-hardware co-design framework of dnns using alternating direction methods of multipliers
Model compression is an important technique to facilitate efficient embedded and hardware
implementations of deep neural networks (DNNs), a number of prior works are dedicated to …
implementations of deep neural networks (DNNs), a number of prior works are dedicated to …
Distributed in-memory computing on binary RRAM crossbar
The recently emerging resistive random-access memory (RRAM) can provide nonvolatile
memory storage but also intrinsic computing for matrix-vector multiplication, which is ideal …
memory storage but also intrinsic computing for matrix-vector multiplication, which is ideal …
Compute-in-memory chips for deep learning: Recent trends and prospects
Compute-in-memory (CIM) is a new computing paradigm that addresses the memory-wall
problem in hardware accelerator design for deep learning. The input vector and weight …
problem in hardware accelerator design for deep learning. The input vector and weight …
Multi-objective optimization of ReRAM crossbars for robust DNN inferencing under stochastic noise
Resistive random-access memory (ReRAM) is a promising technology for designing
hardware accelerators for deep neural network (DNN) inferencing. However, stochastic …
hardware accelerators for deep neural network (DNN) inferencing. However, stochastic …
ReCom: An efficient resistive accelerator for compressed deep neural networks
Deep Neural Networks (DNNs) play a key role in prevailing machine learning applications.
Resistive random-access memory (ReRAM) is capable of both computation and storage …
Resistive random-access memory (ReRAM) is capable of both computation and storage …
BlockGNN: Towards efficient GNN acceleration using block-circulant weight matrices
In recent years, Graph Neural Networks (GNNs) appear to be state-of-the-art algorithms for
analyzing non-euclidean graph data. By applying deep-learning to extract high-level …
analyzing non-euclidean graph data. By applying deep-learning to extract high-level …
Mixed precision quantization for ReRAM-based DNN inference accelerators
ReRAM-based accelerators have shown great potential for accelerating DNN inference
because ReRAM crossbars can perform analog matrix-vector multiplication operations with …
because ReRAM crossbars can perform analog matrix-vector multiplication operations with …