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Conference proceeding
Compiling Spiking Neural Networks to Neuromorphic Hardware
21ST ACM SIGPLAN/SIGBED CONFERENCE ON LANGUAGES, COMPILERS, AND TOOLS FOR EMBEDDED SYSTEMS (LCTES '20)
01 Jan 2020
Abstract
Machine learning applications that are implemented with spike-based computation model, e.g., Spiking Neural Network (SNN), have a great potential to lower the energy consumption when executed on a neuromorphic hardware. However, compiling and mapping an SNN to the hardware is challenging, especially when compute and storage resources of the hardware (viz. crossbars) need to be shared among the neurons and synapses of the SNN. We propose an approach to analyze and compile SNNs on resource-constrained neuromorphic hardware, providing guarantees on key performance metrics such as execution time and throughput. Our approach makes the following three key contributions. First, we propose a greedy technique to partition an SNN into clusters of neurons and synapses such that each cluster can fit on to the resources of a crossbar. Second, we exploit the rich semantics and expressiveness of Synchronous Dataflow Graphs (SDFGs) to represent a clustered SNN and analyze its performance using Max-Plus Algebra, considering the available compute and storage capacities, buffer sizes, and communication bandwidth. Third, we propose a self-timed execution-based fast technique to compile and admit SNN-based applications to a neuromorphic hardware at run-time, adapting dynamically to the available resources on the hardware. We evaluate our approach with standard SNN-based applications and demonstrate a significant performance improvement compared to current practices.
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Details
- Title
- Compiling Spiking Neural Networks to Neuromorphic Hardware
- Creators
- Shihao Song - Drexel UniversityAdarsha Balaji - Drexel UniversityAnup Das - Drexel UniversityNagarajan Kandasamy - Drexel UniversityJames Shackleford - Drexel UniversityACM
- Publication Details
- 21ST ACM SIGPLAN/SIGBED CONFERENCE ON LANGUAGES, COMPILERS, AND TOOLS FOR EMBEDDED SYSTEMS (LCTES '20)
- Publisher
- Assoc Computing Machinery
- Number of pages
- 13
- Grant note
- CCF-1937419 / National Science Foundation Award; National Science Foundation (NSF) CCF-1942697 / National Science Foundation Faculty Early Career Development Award; National Science Foundation (NSF)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000627723300005
- Scopus ID
- 2-s2.0-85086275778
- Other Identifier
- 991019167871004721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Hardware & Architecture
- Computer Science, Software Engineering