Files and links (1)
SubmittedOpen Access (License Unspecified), Open
Conference proceeding
Reliability-Performance Trade-offs in Neuromorphic Computing
2020 11th International Green and Sustainable Computing Workshops (IGSC), pp 1-5
19 Oct 2020
Abstract
Neuromorphic architectures built with Non-Volatile Memory (NVM) can significantly improve the energy efficiency of machine learning tasks designed with Spiking Neural Networks (SNNs). A major source of voltage drop in a crossbar of these architectures are the parasitic components on the crossbar's bitlines and wordlines, which are deliberately made longer to achieve lower cost-per-bit. We observe that the parasitic voltage drops create a significant asymmetry in programming speed and reliability of NVM cells in a crossbar. Specifically, NVM cells that are on shorter current paths are faster to program but have lower endurance than those on longer current paths, and vice versa. This asymmetry in neuromorphic architectures create reliability-performance trade-offs, which can be exploited efficiently using SNN mapping techniques. In this work, we demonstrate such trade-offs using a previously-proposed SNN mapping technique with 10 workloads from contemporary machine learning tasks for a state-of-the art neuromoorphic hardware.
Metrics
10 Record Views
15 citations in Scopus
Details
- Title
- Reliability-Performance Trade-offs in Neuromorphic Computing
- Creators
- Twisha Titirsha - Drexel UniversityAnup Das - Drexel University
- Publication Details
- 2020 11th International Green and Sustainable Computing Workshops (IGSC), pp 1-5
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Scopus ID
- 2-s2.0-85099377081
- Other Identifier
- 991019295198004721