Focus on benchmarks for neuromorphic computing
Bibliographic record
Abstract
Neuromorphic (brain-inspired) computing technology has been of interest to researchers since its origins in the work by a group at CalTech led by Carver Mead in the 1980s. More recently this interest has extended into the commercial domain with major industrial players such as IBM and Intel exploring the technology, and start-up companies commercializing neuromorphic solutions to applications such as inference in low-power edge systems through to datacentre-scale alternatives to GPUs for large language models and deep learning. With this growing commercial interest it is increasingly important to be able to compare and contrast the strengths and weaknesses of alternative neuromorphic offerings that range from the sub-threshold analogue circuits favoured by Mead's seminal work through novel device technologies such as memristors that offer physical in-memory compute capabilities, all the way up to large-scale many-core digital systems based upon conventional (and highly manufacturable) digital technologies. Such comparisons require benchmarks as the basis for comparison, but the sheer diversity of current neuromorphic technologies creates difficulties for prospective benchmarks. This Focus Issue aims to pull together some early thinking on neuromorphic benchmarking. This comes in various forms, including comparing the same application on two different neuromorphic platforms and seeing which applications demonstrate a neuromorphic advantage over conventional solutions. The collected papers represent early perspectives on the neuromorphic benchmarking challenge but they are far from the last words on the matter—there is still a great deal left to do here!
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
Machine scores (provisional)
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.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".