This paper presents a novel architecture for in-memory computation of binary neural network
(BNN) workloads based on STT-MRAM arrays. In the proposed architecture, BNN inputs are …

This paper proposes an in-memory binary spiking neural network (BSNN) based on spin-
transfer-torque magnetoresistive RAM (STT-MRAM). We propose residual BSNN learning …

This work introduces a network architecture NUTS-BSNN: A Non-uniform Time-step
Binarized Spiking Neural Network. NUTS-BSNN is a fully binarized spiking neural network …

With the development of Artificial Intelligence (AI) and Binary neural networks (BNN), the
computing efficiency of the computing system is expected to be much better, however …

Write failure (WF) is a major reliability issue for applications of magnetoresistive random
access memory (MRAM), and much effort has been devoted to reducing the write error rate …

This paper proposes a residual binarized spiking neural network (B-SNN) model suited for
in-memory computing (IMC) implementation. While in most of the prior arts, due to the nature …

Voltage controlled magnetoresistive random access memory (VC MRAM) is a promising
candidate for a future low-power high-density memory. The main causes of bit errors in VC …

Computing-in-memory (CIM) based on spin transfer torque magnetic random access
memory (STT-MRAM) is promised to be an effective way to overcome the" memory wall" …

The implementation of non-volatile memories (NVMs) based in-memory computing (IMC)
have a great potential for neural network applications. Both digital and analog based IMC …