HFNet: A CNN Architecture Co-designed for Neuromorphic Hardware With a Crossbar Array of Synapses.

ABSTRACT: The hardware-software co-optimization of neural network architectures is a field of research that emerged with the advent of commercial neuromorphic chips, such as the IBM TrueNorth and Intel Loihi. Development of simulation and automated mapping software tools in tandem with the design of neuromorphic hardware, whilst taking into consideration the hardware constraints, will play an increasingly significant role in deployment of system-level applications. This paper illustrates the importance and benefits of co-design of convolutional neural networks (CNN) that are to be mapped onto neuromorphic hardware with a crossbar array of synapses. Toward this end, we first study which convolution techniques are more hardware friendly and propose different mapping techniques for different convolutio

SUBMITTER: Gopalakrishnan R 

PROVIDER: S-EPMC7649386 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature