Accelerating Backward Convolution of Convolutional Neural Networks on BWDSP
Bibliographic record
Abstract
Convolutional neural network (CNN), a well-known deep learning architecture extended from articial neural network, has been extensively applied in many applications, which includes image recognition, text classification and robot vision, etc. Inparticular, quite a few inference accelerators have been proposed based on several embedded systems, such as FPGA, ASIC and DSP platforms as for their advantages of fast development round, reconfigurability and high performance, while have less attention to the backpropagation of CNN based on Edge/Embedded System. It is well known that backpropagation is very demanding on hardware resources, and the training platform should have large bandwidth and enough computing resources. That's why it is very strict with the performance requirements. As a result, we focus on the backward convolution of deep CNN in this paper, implement and optimize a deconvolutional alogrithm with loop unrolling, software-pipelined and data partition in multi-macro techniques based on a digital signal processing, combined with its(BWDSP) architecture and instruction features. The experimental results show that our method achieves a performance of 11.07GFLOPS with one core under 500MHz working clock frequency. Then we take VGGNet-19 as a case study to verify our method's effectiveness and compare the efficiency between the accelerator and CPU(Intel Celeron CPU 1005M(@1.90GHz)). Furthermore, we compare it to previous approaches in the final, it turns out that our implementation outperforms better than CPU and FPGA with equivalent computing resources and can be used to deal with scenes with continuous learning requirements.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".