RETRACTED: Benchmarking neuromorphic systems with Nengo
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Post-publication record
- Nature
- Retraction
- Reason
- Concerns/Issues about Data;Miscommunication with/by Author;Objections by Third Party;
- Date
- 11/25/2015 0:00
- Flagged by OpenAlex?
- Yes
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement — it reports them as false, which reads as “fine”.
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
- Teacher spread
- 0.173 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
Nengo is a software package for designing and simulating large-scale neural models. Nengo is architected such that the same Nengo model can be simulated on any of several Nengo backends with few to no modifications. Backends translate a model to specific platforms, which include GPUs and neuromorphic hardware. Nengo also contains a large test suite that can be run with any backend and focuses primarily on functional performance. We propose that Nengo's large test suite can be used to benchmark neuromorphic hardware's functional performance and simulation speed in an efficient, unbiased, and future-proof manner. We implement four benchmark models and show that Nengo can collect metrics across five different backends that identify situations in which some backends perform more accurately or quickly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- Frontiers in Neuroscience
- Topic
- Advanced Memory and Neural Computing
- Field
- Engineering
- Canadian institutions
- University of Waterloo
- Funders
- Air Force Office of Scientific ResearchOffice of Naval ResearchNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
- Keywords
- Benchmark (surveying)Computer scienceSuiteBenchmarkingNeuromorphic engineeringSoftwareTest suiteArtificial intelligenceComputer architectureMachine learningArtificial neural networkTest caseProgramming language
- Has abstract in OpenAlex
- yes