Memristor based neuromorphic computing systems give alternative solutions to boost the computing energy efficiency of Neural Network (NN) algorithms. Because of the large-scale applications and the large architecture design space, many factors will ...

Utilizing emerging nonvolatile memories to accelerate deep neural network (DNN) has been considered as one of the promising approaches to solve the bottleneck of data transfer during the multiplication and accumulation (MAC). Among them, spintronic ...

In this work, we review two alternative Processing-in-Memory (PIM) accelerators based on Spin-Orbit-Torque Magnetic Random Access Memory (SOT-MRAM) to execute DNA short read alignment based on an optimized and hardware-friendly alignment algorithm. We ...

In the post-Moore era, in order to solve the problem of von Neumann bottleneck and memory wall caused by separation of memory and processor, in-memory-processing (IMP) technique has aroused great attention. Novel non-volatile memory (NVM) based on ...

Resistive RAM (ReRAM) is a promising technology with such advantages as small device size and in-memory-computing capability. However, designing optimal AI processors based on ReRAMs is challenging due to the limited precision, and the complex interplay ...

This paper proposes a novel in-memory computing (IMC) scheme based on toggle spin torque magnetic random access memory (TST-MRAM), called TST-IMC, which makes full use of the unique TST writing mechanism. In this scheme, all of the computing results are ...