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 …
Cost-and dataset-free stuck-at fault mitigation for ReRAM-based deep learning accelerators
G Jung, M Fouda, S Lee, J Lee… - … Design, Automation & …, 2021 - ieeexplore.ieee.org
Resistive RAMs can implement extremely efficient matrix vector multiplication, drawing much
attention for deep learning accelerator research. However, high fault rate is one of the …
attention for deep learning accelerator research. However, high fault rate is one of the …
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 …
Bit-transformer: Transforming bit-level sparsity into higher preformance in reram-based accelerator
Resistive Random-Access-Memory (ReRAM) crossbar is one of the most promising neural
network accelerators, thanks to its in-memory and in-situ analog computing abilities for …
network accelerators, thanks to its in-memory and in-situ analog computing abilities for …
Att: A fault-tolerant reram accelerator for attention-based neural networks
H Guo, L Peng, J Zhang, Q Chen… - 2020 IEEE 38th …, 2020 - ieeexplore.ieee.org
Crossbar-based resistive RAM has been widely used in deep learning accelerator designs
because it largely eliminates weight movement between memory and processing units. The …
because it largely eliminates weight movement between memory and processing units. The …
Sme: Reram-based sparse-multiplication-engine to squeeze-out bit sparsity of neural network
Resistive Random-Access-Memory (ReRAM) cross-bar is a promising technique for deep
neural network (DNN) accelerators, thanks to its in-memory and in-situ analog computing …
neural network (DNN) accelerators, thanks to its in-memory and in-situ analog computing …
Robust RRAM-based in-memory computing in light of model stability
Resistive random-access memory (RRAM)-based in-memory computing (IMC) architectures
offer an energy-efficient solution for DNN acceleration. However, the performance of RRAM …
offer an energy-efficient solution for DNN acceleration. However, the performance of RRAM …
DL-RSIM: A simulation framework to enable reliable ReRAM-based accelerators for deep learning
MY Lin, HY Cheng, WT Lin, TH Yang… - 2018 IEEE/ACM …, 2018 - ieeexplore.ieee.org
Memristor-based deep learning accelerators provide a promising solution to improve the
energy efficiency of neuromorphic computing systems. However, the electrical properties …
energy efficiency of neuromorphic computing systems. However, the electrical properties …
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 …
Defects mitigation in resistive crossbars for analog vector matrix multiplication
With storage and computation happening at the same place, computing in resistive
crossbars minimizes data movement and avoids the memory bottleneck issue. It leads to …
crossbars minimizes data movement and avoids the memory bottleneck issue. It leads to …