A stochastic computing implementation of the disparity energy model for depth perception
Bibliographic record
Abstract
Implementing neuromorphic algorithms is increasingly interesting as the error resilience and low-area, low-energy nature of biological systems becomes the potential solution for problems in robotics and artificial intelligence. While conventional digital methods are inefficient in implementing massively parallel systems, analog solutions are hard to design and program. Stochastic computing (SC) is a natural bridge that allows pseudo-analog computations in the digital domain using low-complexity hardware. However, large-scale SC systems traditionally suffered from long latencies, hence higher energy consumption. This thesis develops a VLSI architecture for an SC based binocular vision system based on a disparity-energy model that emulates the hierarchical multi-layered neural structure in the primary visual cortex. The architecture is compact, adder-free and achieves better disparity detection at very low latencies compared to a floating-point version by using a modified disparity-energy model. A 1D 1x100 pixel processing system is synthesized using TSMC 65nm CMOS technology and achieves a reduction of 84% in area-delay product and 62% in energy compared to a fixed point implementation without error normalization. Further, at the same error rate, the generalized 2D stochastic architecture proposed to ease reusability achieves 89% reduction in area-delay product and 81% in energy savings compared to a fixed-point implementation. This demonstrates stochastic computing to be a strong candidate to implement large-scale neuromorphic algorithms efficiently. We also presented several insights into the differences and similarities between IBM's TrueNorth architecture and stochastic computing, proposing potential venues for future research.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".