RISC-V Barrel Processor for Deep Neural Network Acceleration
Bibliographic record
Abstract
This paper presents a barrel RISC-V processor designed to control a deep neural network accelerator. Our design has a 5-stage pipeline data path with 8 hardware threads (harts). Each thread is executed under a strict round robin scheduler and is responsible for providing data and control signals to a neural network processing element (PE). Each PE is capable of arbitrary precision GEneral Matrix Vector (GEMV) operations. The execution of each thread is independent of other threads and any communication between threads are sent through shared memory via software. To reduce the area required for implementation, our processor is an implementation of the RV32I plus a set of custom CSRs for controlling the PEs. Our design passes all riscv_test written in assembly and compiled with RISC-V gcc. Our 8-hart barrel processor runs at 250 MHz with CPI of 1 and consumes 0.372W. To demonstrate the capabilities of our design, we computed a GEMV operation with an input matrix size of 8 by 128 and a weight matrix size of 128 by 128 with two-bit precision in only 16 clock cycles.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".