Journal article
Mapping Spiking Neural Networks to Neuromorphic Hardware
IEEE transactions on very large scale integration (VLSI) systems, v 28(1), pp 76-86
Jan 2020
Abstract
Neuromorphic hardware implements biological neurons and synapses to execute a spiking neural network (SNN)-based machine learning. We present SpiNeMap, a design methodology to map SNNs to crossbar-based neuromorphic hardware, minimizing spike latency and energy consumption. SpiNeMap operates in two steps: SpiNeCluster and SpiNePlacer. SpiNeCluster is a heuristic-based clustering technique to partition an SNN into clusters of synapses, where intracluster local synapses are mapped within crossbars of the hardware and intercluster global synapses are mapped to the shared interconnect. SpiNeCluster minimizes the number of spikes on global synapses, which reduces spike congestion and improves application performance. SpiNePlacer then finds the best placement of local and global synapses on the hardware using a metaheuristic-based approach to minimize energy consumption and spike latency. We evaluate SpiNeMap using synthetic and realistic SNNs on a state-of-the-art neuromorphic hardware. We show that SpiNeMap reduces average energy consumption by 45% and spike latency by 21%, compared to the best-performing SNN mapping technique.
Metrics
Details
- Title
- Mapping Spiking Neural Networks to Neuromorphic Hardware
- Creators
- Adarsha Balaji - Drexel UniversityFrancky Catthoor - ImecAnup Das - Drexel UniversityYuefeng Wu - Imec the NetherlandsKhanh Huynh - Imec the NetherlandsFrancesco G Dell'Anna - Imec the NetherlandsGiacomo Indiveri - University of ZurichJeffrey L Krichmar - University of California, IrvineNikil D Dutt - University of California, IrvineSiebren Schaafsma - Imec the Netherlands
- Publication Details
- IEEE transactions on very large scale integration (VLSI) systems, v 28(1), pp 76-86
- Publisher
- IEEE
- Grant note
- CCF-1937419 / National Science Foundation (10.13039/501100008982)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000506608100008
- Scopus ID
- 2-s2.0-85077296614
- Other Identifier
- 991019238706304721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Hardware & Architecture
- Engineering, Electrical & Electronic