FangTianSim: High-Level Cycle-Accurate Resistive Random-Access Memory-Based Multi-Core Spiking Neural Network Processor Simulator.

ABSTRACT: Realization of spiking neural network (SNN) hardware with high energy efficiency and high integration may provide a promising solution to data processing challenges in future internet of things (IoT) and artificial intelligence (AI). Recently, design of multi-core reconfigurable SNN chip based on resistive random-access memory (RRAM) is drawing great attention, owing to the unique properties of RRAM, e.g., high integration density, low power consumption, and processing-in-memory (PIM). Therefore, RRAM-based SNN chip may have further improvements in integration and energy efficiency. The design of such a chip will face the following problems: significant delay in pulse transmission due to complex logic control and inter-core communication; high risk of digital, analog, and RRAM hybrid desig

SUBMITTER: Wei J 

PROVIDER: S-EPMC8811373 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature