Automatic transistor and physical design of FPGA tiles from an architectural specification
Why this work is in the frame
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Bibliographic record
Abstract
One of the most difficult and time-consuming steps in the creation of an FPGA is its transistor-level design and physical layout. Modern commercial FPGAs typically consume anywhere from 50 to 200 man-years simply in the layout step. To date, automated tools have only been employed in small parts of the periphery and programming circuitry. The core tiles, which are repeated many times, are subject to painstaking manual design and layout. In this paper we present a new system (called GILES, for Good Instant Layout of Erasable Semiconductors) that automatically generates a transistor-level schematic from a high-level architectural specification of an FPGA. It also generates a cell-level netlist that is placed and routed automatically. The architectural specification is the one used as input to the VPR [3] architectural exploration tool. The output is the mask-level layout of a single tile that can be replicated to form an FPGA array. We describe a new placement tool that simultaneously places and compacts the layout to minimize white space and wiring demand, and a special-purpose router built for this task.GILES can place and route a tile consisting of four 4-input LUT logic cells and all of its programmable wires in a 0.18μm CMOS process using 8 layers of metal and 25983μm2 of area. When we generate the layout of an architecture similar to the Xilinx Virtex-E FPGA (built in a 0.18μm process) GILES requires only 47% more area than the original. The layout area of an architecture similar to the Altera Apex 20K400E (also built in a 0.18µm process) constructed by GILES requires 97% more area than the original.
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