[HTML][HTML] Analog architectures for neural network acceleration based on non-volatile memory
Analog hardware accelerators, which perform computation within a dense memory array,
have the potential to overcome the major bottlenecks faced by digital hardware for data …
have the potential to overcome the major bottlenecks faced by digital hardware for data …
Improving the robustness of analog deep neural networks through a Bayes-optimized noise injection approach
Analog deep neural networks (DNNs) provide a promising solution, especially for
deployment on resource-limited platforms, for example in mobile settings. However, the …
deployment on resource-limited platforms, for example in mobile settings. However, the …
An accurate, error-tolerant, and energy-efficient neural network inference engine based on SONOS analog memory
We demonstrate SONOS (silicon-oxide-nitride-oxide-silicon) analog memory arrays that are
optimized for neural network inference. The devices are fabricated in a 40nm process and …
optimized for neural network inference. The devices are fabricated in a 40nm process and …
Benchmarking inference performance of deep learning models on analog devices
O Fagbohungbe, L Qian - 2021 International Joint Conference …, 2021 - ieeexplore.ieee.org
Analog hardware implemented deep learning models are promising for computation and
energy constrained systems such as edge computing devices. However, the analog nature …
energy constrained systems such as edge computing devices. However, the analog nature …
In-memory computing array using 40nm multibit SONOS achieving 100 TOPS/W energy efficiency for deep neural network edge inference accelerators
V Agrawal, V Prabhakar, K Ramkumar… - 2020 IEEE …, 2020 - ieeexplore.ieee.org
40nm SONOS (Si-Oxide-Nitride-Oxide-Si) based non-volatile memory (NVM) cell has been
evaluated for analog memory to perform in-memory neuromorphic computing. Process flow …
evaluated for analog memory to perform in-memory neuromorphic computing. Process flow …
The Effect of Batch Normalization on Noise Resistant Property of Deep Learning Models
O Fagbohungbe, L Qian - IEEE Access, 2022 - ieeexplore.ieee.org
The fast execution speed and energy efficiency of analog hardware have made them a
strong contender for deploying deep learning models at the edge. However, there are …
strong contender for deploying deep learning models at the edge. However, there are …
Ionizing radiation effects in SONOS-based neuromorphic inference accelerators
We evaluate the sensitivity of neuromorphic inference accelerators based on silicon-oxide-
nitride-oxide-silicon (SONOS) charge trap memory arrays to total ionizing dose (TID) effects …
nitride-oxide-silicon (SONOS) charge trap memory arrays to total ionizing dose (TID) effects …
Effect of layer-specific synaptic retention characteristics on the accuracy of deep neural networks
By modeling the change of weight/bias over time due to the retention behavior of charge trap
device (CTD), we study the influence of synaptic retention characteristics on the inference …
device (CTD), we study the influence of synaptic retention characteristics on the inference …
Impact of TID on the Analog Conductance and Training Accuracy of CBRAM-Based Neural Accelerator
The changes caused by total ionizing dose (TID) in the conductance of the analog response
of Ag-Ge30Se70 conductive bridge random access memory (CBRAM) based synapses are …
of Ag-Ge30Se70 conductive bridge random access memory (CBRAM) based synapses are …
A binary-activation, multi-level weight RNN and training algorithm for ADC-/DAC-free and noise-resilient processing-in-memory inference with eNVM
We propose a new algorithm for training neural networks with binary activations and multi-
level weights, which enables efficient processing-in-memory circuits with embedded …
level weights, which enables efficient processing-in-memory circuits with embedded …