RAM-JET: Towards The Removal Of Multiplicative Compleidty In Digital Signal Processingvlst Arclutectures
Bibliographic record
Abstract
The concept of a programmable systolic cell is introduced that allows finite ring arithmetic to be performed using bit-level systolic arrays. The motivation for this work is the recent introduction of a fixed coefficient bit-level inner product step processor operating over a finite ring (BIPSPm). From design and fabrication experiments performed on this cell, it is clear that it represents a major step forward in the implementation of very high speed fine-grained DSP algorithms. The remarkable properties of the cell are the ability to implement fixed product multiplication with only twice the area overhead of a bit-level binary adder, and yet provide operation at slightly higher throughput rates. The cell also has low overhead associated with error detection, and an entire array of an arbitrary number of cells in a linear pipeline, can be tested with only 32 test vectors in the time taken to pass the vectors through the array. This paper discusses a programmable system that will operate with a modified cell, to allow arbitrary algorithms to be implemented without incurring the overhead associated with general multiplication. Rather than seek algorithms that minimize multiplication, we can rather seek algorithms that provide a homogeneous VLSI structure.
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.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".