The emergence of artificial intelligence has represented great potential in solving a wide
range of complex problems. However, traditional general-purpose chips based on von …

Spiking neural networks (SNNs), as one of the algorithmic models in neuromorphic
computing, have gained a great deal of research attention owing to temporal information …

This paper proposes a new pre-conditioned spike-timing-dependent plasticity (STDP)
learning rule as well as an efficient system-level time-domain circuit modeling and …

Resistive random access memory (RRAM) is a promising emerging nonvolatile memory
(NVM) due to its large resistance ratio in different switching states. To improve memory …

This paper provides a comprehensive study of signal integrity issues in RRAM-based
neuromorphic chip crossbar arrays due to interconnect parasitic. First, the parasitic …

This paper proposes a complete CMOS realized neuromorphic system for pattern
recognition having CMOS-based memristor emulators as synaptic circuits. The crossbar …

Nonvolatile memory (NVM) based neural network can directly perform in situ computation in
memory to significantly reduce energy consumption resulting from the data movement …

This paper presents the efficient systematic methods for modeling and analysis of spike
signal sequence in crossbar arrays for neuromorphic computing chips. A novel spike signal …

With the rapid advances of artificial intelligence and its applications, the design of memristor-
based neuromorphic chips inspired by the human brain has become an important area of …

Neuromorphic hardware has become hotspot in the field of brain-like computing due to its
advantages. However, the presence of external noise imposes challenges with respect to …