期刊
出版社
IEEE
关键词
-
类别
资金
- Sorbonne Center for Artificial Intelligence (SCAI)
- Penta HADES project
- Junta de Andalucia [US-1260118]
- VI PPIT through the Universidad de Sevilla
This paper assesses the resilience characteristics of a hardware accelerator for Spiking Neural Networks (SNNs) and identifies the parts of the design that need protection against faults and the parts that are inherently fault-tolerant.
Despite the parallelism and sparsity in neural network models, their transfer into hardware unavoidably makes them susceptible to hardware-level faults. Hardware-level faults can occur either during manufacturing, such as physical defects and process-induced variations, or in the field due to environmental factors and aging. The performance under fault scenarios needs to be assessed so as to develop cost-effective fault-tolerance schemes. In this work, we assess the resilience characteristics of a hardware accelerator for Spiking Neural Networks (SNNs) designed in VHDL and implemented on an FPGA. The fault injection experiments pinpoint the parts of the design that need to be protected against faults, as well as the parts that are inherently fault-tolerant.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据