A multi-FPGA architecture for stochastic Restricted Boltzmann Machines
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
Although there are many neural network FPGA architectures, there is no framework for designing large, high-performance neural networks suitable for the real world. In this paper, we present two concepts to support a multi-FPGA architecture for stochastic restricted Boltzmann machines (RBM), a popular type of neural network. First, a hardware core, called the kth stage piecewise linear interpolator, is used to implement a high-precision, pipelined function generator. The interpolator increases the resolution of a look up table implementation, guaranteeing an additional bit of precision for every pipeline stage. This function generator is used to implement a sigmoid function required in stochastic node selection. Next, a partitioning algorithm is used to efficiently divide a RBM amongst multiple FPGAs. The partitioning algorithm optimizes performance by minimizing the inter-FPGA communication. The architecture is tested on the Berkeley Emulation Engine 2 running at 100 MHz. One board supports a RBM of 256 times 256 nodes, and results in a computational speed of 1.85 billion connection-updatesper- second and a speed-up of 85-fold over an optimized C program running on a 2.8 GHz Intel processor.
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