Deep learning enabled state of charge, state of health and remaining useful life estimation for smart battery management system: Methods, implementations, issues …
State of Charge (SOC), state of health (SOH), and remaining useful life (RUL) are the crucial
indexes used in the assessment of electric vehicle (EV) battery management systems (BMS) …
indexes used in the assessment of electric vehicle (EV) battery management systems (BMS) …
Intelligent SOX estimation for automotive battery management systems: state-of-the-art deep learning approaches, open issues, and future research opportunities
Real-time battery SOX estimation including the state of charge (SOC), state of energy (SOE),
and state of health (SOH) is the crucial evaluation indicator to assess the performance of …
and state of health (SOH) is the crucial evaluation indicator to assess the performance of …
A survey on the optimization of neural network accelerators for micro-ai on-device inference
Deep neural networks (DNNs) are being prototyped for a variety of artificial intelligence (AI)
tasks including computer vision, data analytics, robotics, etc. The efficacy of DNNs coincides …
tasks including computer vision, data analytics, robotics, etc. The efficacy of DNNs coincides …
A 28nm 27.5 TOPS/W approximate-computing-based transformer processor with asymptotic sparsity speculating and out-of-order computing
Y Wang, Y Qin, D Deng, J Wei, Y Zhou… - … solid-state circuits …, 2022 - ieeexplore.ieee.org
Recently, Transformer-based models have achieved tremendous success in many AI fields,
from NLP to CV, using the attention mechanism 1–3. This mechanism captures the global …
from NLP to CV, using the attention mechanism 1–3. This mechanism captures the global …
A surrogate gradient spiking baseline for speech command recognition
Artificial neural networks (ANNs) are the basis of recent advances in artificial intelligence
(AI); they typically use real valued neuron responses. By contrast, biological neurons are …
(AI); they typically use real valued neuron responses. By contrast, biological neurons are …
Spartus: A 9.4 TOp/s FPGA-based LSTM accelerator exploiting spatio-temporal sparsity
Long short-term memory (LSTM) recurrent networks are frequently used for tasks involving
time-sequential data, such as speech recognition. Unlike previous LSTM accelerators that …
time-sequential data, such as speech recognition. Unlike previous LSTM accelerators that …
EdgeDRNN: Recurrent neural network accelerator for edge inference
Low-latency, low-power portable recurrent neural network (RNN) accelerators offer powerful
inference capabilities for real-time applications such as IoT, robotics, and human-machine …
inference capabilities for real-time applications such as IoT, robotics, and human-machine …
Tinyvers: A tiny versatile system-on-chip with state-retentive eMRAM for ML inference at the extreme edge
Extreme edge devices or Internet-of-Things (IoT) nodes require both ultra-low power (ULP)
always-on (AON) processing as well as the ability to do on-demand sampling and …
always-on (AON) processing as well as the ability to do on-demand sampling and …
Digital versus analog artificial intelligence accelerators: Advances, trends, and emerging designs
For state-of-the-art artificial intelligence (AI) accelerators, there have been large advances in
both all-digital and analog/mixed-signal circuit-based designs. This article presents a …
both all-digital and analog/mixed-signal circuit-based designs. This article presents a …
An energy-efficient transformer processor exploiting dynamic weak relevances in global attention
Y Wang, Y Qin, D Deng, J Wei, Y Zhou… - IEEE Journal of Solid …, 2022 - ieeexplore.ieee.org
Transformer-based models achieve tremendous success in many artificial intelligence (AI)
tasks, outperforming conventional convolution neural networks (CNNs) from natural …
tasks, outperforming conventional convolution neural networks (CNNs) from natural …