Recently we have shown that an architecture based on resistive processing unit (RPU) devices has potential to achieve significant acceleration in deep neural network (DNN) training compared to today's software-based DNN implementations running on CPU/GPU. However, currently available device candidates based on non-volatile memory technologies do not satisfy all the requirements to realize the RPU concept. Here, we propose an analog CMOS-based RPU design (CMOS RPU) which can store and process data locally and can be operated in a massively parallel manner. We analyze various properties of the CMOS RPU to evaluate the functionality and feasibility for acceleration of DNN training.1
Analog switching memristive devices can be used as part of the acceleration block of Neural Network...
Metal-oxide-based resistive memory devices (ReRAM) are being actively researched as synaptic element...
Crossbar arrays of non-volatile memory (NVM) devices represent one possible path for implementing hi...
Novel Deep Neural Network (DNN) accelerators based on crossbar arrays of non-volatile memories (NVMs...
There is an urgent need for compact, fast, and power-efficient hardware implementations of state-of-...
As AI applications become more prevalent and powerful, the performance of deep learning neural netwo...
Deep Neural Networks (DNNs) have become a promising solution to inject AI in our daily lives from se...
Deep neural networks (DNNs) have achieved unprecedented capabilities in tasks such as analysis and r...
Deep neural networks (DNNs) have achieved unprecedented capabilities in tasks such as analysis and r...
Deep Neural Networks (DNNs) have become a promising solution to inject AI in our daily lives from se...
The von Neumann architecture has been broadly adopted in modern computing systems in which the centr...
In our previous work we have shown that resistive cross point devices, so called resistive processin...
The von Neumann architecture has been broadly adopted in modern computing systems in which the centr...
<p>In recent years, neuromorphic architectures have been an increasingly effective tool used to solv...
There is an urgent need for compact, fast, and power-efficient hardware implementations of state-of-...
Analog switching memristive devices can be used as part of the acceleration block of Neural Network...
Metal-oxide-based resistive memory devices (ReRAM) are being actively researched as synaptic element...
Crossbar arrays of non-volatile memory (NVM) devices represent one possible path for implementing hi...
Novel Deep Neural Network (DNN) accelerators based on crossbar arrays of non-volatile memories (NVMs...
There is an urgent need for compact, fast, and power-efficient hardware implementations of state-of-...
As AI applications become more prevalent and powerful, the performance of deep learning neural netwo...
Deep Neural Networks (DNNs) have become a promising solution to inject AI in our daily lives from se...
Deep neural networks (DNNs) have achieved unprecedented capabilities in tasks such as analysis and r...
Deep neural networks (DNNs) have achieved unprecedented capabilities in tasks such as analysis and r...
Deep Neural Networks (DNNs) have become a promising solution to inject AI in our daily lives from se...
The von Neumann architecture has been broadly adopted in modern computing systems in which the centr...
In our previous work we have shown that resistive cross point devices, so called resistive processin...
The von Neumann architecture has been broadly adopted in modern computing systems in which the centr...
<p>In recent years, neuromorphic architectures have been an increasingly effective tool used to solv...
There is an urgent need for compact, fast, and power-efficient hardware implementations of state-of-...
Analog switching memristive devices can be used as part of the acceleration block of Neural Network...
Metal-oxide-based resistive memory devices (ReRAM) are being actively researched as synaptic element...
Crossbar arrays of non-volatile memory (NVM) devices represent one possible path for implementing hi...
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