A Novel Quantization and Model Compression Approach for Hardware Accelerators in Edge Computing
Bibliographic record
Abstract
Massive computational complexity and memory requirement of artificial intelligence models impede their deployability on edge computing devices of the Internet of Things (IoT). While Power-of-Two (PoT) quantization is proposed to improve the efficiency for edge inference of Deep Neural Networks (DNNs), existing PoT schemes require a huge amount of bit-wise manipulation and have large memory overhead, and their efficiency is bounded by the bottleneck of computation latency and memory footprint. To tackle this challenge, we present an efficient inference approach on the basis of PoT quantization and model compression. An integer-only scalar PoT quantization (IOS-PoT) is designed jointly with a distribution loss regularizer, wherein the regularizer minimizes quantization errors and training disturbances. Additionally, two-stage model compression is developed to effectively reduce memory requirement, and alleviate bandwidth usage in communications of networked heterogenous learning systems. The product look-up table (P-LUT) inference scheme is leveraged to replace bit-shifting with only indexing and addition operations for achieving low-latency computation and implementing efficient edge accelerators. Finally, comprehensive experiments on Residual Networks (ResNets) and efficient architectures with Canadian Institute for Advanced Research (CIFAR), ImageNet, and Real-world Affective Faces Database (RAF-DB) datasets, indicate that our approach achieves 2×∼10× improvement in the reduction of both weight size and computation cost in comparison to state-of-the-art methods. A P-LUT accelerator prototype is implemented on the Xilinx KV260 Field Programmable Gate Array (FPGA) platform for accelerating convolution operations, with performance results showing that P-LUT reduces memory footprint by 1.45×, achieves more than 3× power efficiency and 2× resource efficiency, compared to the conventional bit-shifting scheme.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".