Haptic rendering of deformable objects using a multiple FPGA parallel computing architecture
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
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.
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 | 0.001 | 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