Neuromorphic systems are expected to be a breakthrough beyond the conventional von
Neumann architecture when implementing an artificial neural network. In a neuromorphic …

In this work, we present a scheme for implementing learning on a digital non-volatile
memory (NVM) based hardware accelerator for Spiking Neural Networks (SNNs). Our …

Processing circuitry for a deep neural network can include input/output ports, and a plurality
of neural network layers coupled in order from a first layer to a last layer, each of the plurality …

Analog non-volatile memory (NVM)-based accelerators for Deep Neural Networks (DNNs)
can achieve high-throughput and energy-efficient multiply-accumulate (MAC) operations by …

Since its inception, resistive random access memory (RRAM) has widely been regarded as
a promising technology, not only for its potential to revolutionize non-volatile data storage by …

Non-volatile analog memory and in-memory computing have great potential to enable high-
performance Deep Neural Network (DNN) inference accelerators with significantly better …

Due to recent hardware and software technological advancements, machine learning (ML)
algorithms have been successfully used to reinvigorate many research fields, ranging from …

(57) ABSTRACT A multiply and accumulate calculation device includes a multiple
calculation unit and a accumulate calculation unit. The multiple calculation unit includes a …

Neuromorphic computation based on analog nonvolatile memories (NVMs) holds great
promise to improve Deep Neural Networks inference performance. In virtue of an …