The neurobench framework for benchmarking neuromorphic computing algorithms and systems
Harvard University Press · Delft University of Technology · +45 more institutions
Abstract
Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promising future research directions. This article presents NeuroBench, a benchmark framework for neuromorphic algorithms and systems, which is collaboratively designed from an open community of researchers across industry and academia. NeuroBench introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an…
Citation impact
- FWCI
- 34.82
- Percentile
- 100%
- References
- 83
Authors
100Topics & keywords
- Neuromorphic engineering
- Benchmarking
- Benchmark (surveying)
- Computer science
- Set (abstract data type)
- Computer architecture
- Field (mathematics)
- Data science
- Industry, innovation and infrastructure