Haptic rendering of deformable objects using a multiple FPGA parallel computing architecture
Bibliographic record
Abstract
High-fidelity simulations of haptic interaction with deformable objects is computationally challenging. In this paper, hardwarebased parallel computing is proposed for finite-element (FE) analysis of soft-object deformation models. A distributed implementation of the Preconditioned Conjugate Gradient (PCG) algorithms on N Field Programmable Gate Array (FPGA) devices can solve the large system of equations arising from FE models at high update rates required for stable haptic interaction. Massive parallelization of the computations is achieved by customizing the hardware architecture to the problem at hand and concurrently employing a large number of adaptive fixed-point computing units. An implementation of this scalable hardware accelerator on four Altera EP3SE110 FPGA devices is capable of performing 230.4 Giga Operations per second in Sparse Matrix by Vector (SpMxV) multiplication. This architecture has successfully enabled real-time simulation of haptic interaction with a 3-dimensional FE model of 6000 nodes at an update rate of 200 Hz. Both static and dynamic linear elastic models have been successfully simulated.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".