Fault-Tolerant-Driven Clustering for Large Scale Neuromorphic Computing Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Memristive crossbar-based neuromorphic computing systems (NCS) have been extensively investigated and applied to the neural networks due to the fast computation and low design cost. In most applications, neural networks are large and sparse, which violate the size limitations and high-density connections provided by the memristive crossbars. Besides, stuck-at-faults (SAFs) in the memristor devices significantly degrade the computing accuracy of NCS. In this paper, we propose a fault-driven clustering framework for NCS based on a set of unique size memristive crossbars, with consideration of both hardware cost and mapping success rate. First, in order to group the input neurons connected to different output neurons, we design a METIS-based clustering method by redefining the distance metric, to speed up the large-scale neural network partitioning and improve the fault tolerance of the memristive crossbar-based NCS, then map the synapses to a set of unique size crossbars. Second, a half transposition method is developed to address the extremely asymmetric clusters. The simulation results show that the proposed fault tolerance-aware clustering algorithm not only improves the mapping success rate and the hardware cost but also achieves speed-up. For example, for a large-scale neural network with four million synapses, the proposed framework can complete the algorithm in one hour.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it