ArticleFrontiers in neuroscience2022
Benchmarking Neuromorphic Hardware and Its Energy Expenditure.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed, 20 citations in OpenAlex.
- Self-Oscillatory Neuron-like Devices for Unconventional Computing Applications.Chemical reviews · 2026Review
- Neuromorphic Near-Sensor and In-Sensor Computing Enabled by Next-Generation Material-Based Sensors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Memristor Synapse-A Device-Level Critical Review.Nanomaterials (Basel, Switzerland) · 2026Review
- The road to commercial success for neuromorphic technologies.Nature communications · 2025Review
- The neurobench framework for benchmarking neuromorphic computing algorithms and systems.Nature communications · 2025Review
- Polariton lattices as binarized neuromorphic networks.Light, science & applications · 2025Article
- Efficient parameter calibration and real-time simulation of large-scale spiking neural networks with GeNN and NEST.Frontiers in neuroinformatics · 2023Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We propose and discuss a platform overarching benchmark suite for neuromorphic hardware. This suite covers benchmarks from low-level characterization to high-level application evaluation using benchmark specific metrics. With this rather broad approach we are able to compare various hardware systems including mixed-signal and fully digital neuromorphic architectures. Selected benchmarks are discussed and results for several target platforms are presented revealing characteristic differences between the various systems. Furthermore, a proposed energy model allows to combine benchmark performance metrics with energy efficiency. This model enables the prediction of the energy expenditure of a network on a target system without actually having access to it. To quantify the efficiency gap between neuromorphics and the biological paragon of the human brain, the energy model is used to estimate the energy required for a full brain simulation. This reveals that current neuromorphic systems are at least four orders of magnitude less efficient. It is argued, that even with a modern fabrication process, two to three orders of magnitude are remaining. Finally, for selected benchmarks the performance and efficiency of the neuromorphic solution is compared to standard approaches.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.