cuSCNN : an Efficient CUDA Implementation of Sparse CNNs
Bibliographic record
Abstract
Deep Neural Network models are becoming much larger which greatly increases their computation and memory requirements. Sparsity offers great opportunities to reduce unnecessary data transfers and computations. However, exploiting sparsity in CNN inference presents challenges such as irregularities in memory access patterns. To overcome this challenge, we propose cuSCNN, an efficient sparse CNN inference engine that leverages the sparsity of both models and activations using optimized sparse-sparse matrix convolution kernels with compressed operands. cuSCNN is motivated by the concepts introduced by the SCNN hardware accelerator[21] but modified appropriately to achieve an efficient software implementation for GPUs. We develop GPU optimizations that boost execution performance and reduce the required memory size and bandwidth. cuSCNN achieves a speedup of up to 171 × compared to an efficient CPU implementation and 30 × speedup compared to a multi-threaded CPU implementation without batching, enabling the use of inexpensive low-end memory-constrained GPUs to implement large networks with near real-time latency. Although GPU throughput can benefit from larger batch sizes, batch size 1 achieves the lowest latency and hence we focus on it.
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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 | 0.001 |
| 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".