MSPARQ: A RISC-V Vector Processor Array Optimized for Low-Resolution Neural Networks
Bibliographic record
Abstract
This paper explores using a multicore RISC- V vector processor to enhance the performance of convolutional neural networks (CNNs) quantized to low and ultra-low precisions. Optimizing CNNs' efficiency is crucial in resource-constrained environments such as embedded systems. To this end, we use Sparq, a 64-bit RISC-V RVV1.0 processor derived from “Ara”. This paper introduces MSPARQ, its multicore version developed using the OpenPiton framework and implemented in GlobalFoundries 22FDX FD-SOI technology. The performance of a CNN conv2d kernel is evaluated by emulating MSPARQ on a Xilinx Alveo U280 FPGA board. Results show that through an adequate mapping of this kernel, a 4-core configuration can achieve a speedup of 3.6 to 3.8 times over the single-core configuration, while sub-byte optimizations included in the Sparq core deliver an additional speedup by 2.1 and 1.8 times respectively for 1-bit and 2-bit precisions, over the 16-bit implementation of the conv2d operation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".