Mix-GEMM: Extending RISC-V CPUs for Energy-Efficient Mixed-Precision DNN Inference Using Binary Segmentation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Efficiently computing Deep Neural Networks (DNNs) has become a primary challenge in today's computers, especially on devices targeting mobile or edge applications. Recent progress on Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT) has shown that the key to high energy efficiency lies in executing deep learning models with low- (8- to 5-bit) or ultra-low-precision (4- to 2-bit). Unfortunately, current Central Processing Unit (CPU) architectures and Instruction Set Architectures (ISAs) present severe limitations on the range of data sizes supported to compute DNN kernels. In this work, we present <i>Mix-GEMM</i>, a hardware-software co-designed architecture that enables RISC-V processors to efficiently compute arbitrary mixed-precision DNN kernels, supporting all data size combinations from 8- to 2-bit. By applying <i>binary segmentation</i>, our architecture can scale its throughput by decreasing the data size of the operands, resulting in a flexible approach capable of leveraging state-of-the-art QAT and PTQ to achieve high energy efficiency at a very low cost. Evaluating our <i>Mix-GEMM</i> architecture in a dual-issue in-order RISC-V processor shows that we are able to boost its performance and energy efficiency by up to <inline-formula><tex-math notation="LaTeX">$44\times$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$11\times$</tex-math></inline-formula> with respect to the baseline processor, with an area overhead of only 2%. This allows our extended processor to execute state-of-the-art DNNs with significantly higher performance and energy efficiency than the standard FP32 precision, while retaining almost the same model accuracy.
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