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 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 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".