We present a theoretical analysis and empirical evaluations of a novel set of techniques for computational cost reduction of classifiers that are based on learned transform and soft-threshold. By modifying optimization procedures for dictionary and classifier training, as well as the resulting dictionary entries, our t…
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6 results for “bit-shift”
The paper proposes a dataflow-based approach to reduce energy consumption in deep neural networks without sacrificing performance.
problem Reducing energy consumption in deep neural networks without performance drop.
method Dataflow-based joint quantization of weights and activations.
result Joint quantization improves performance and reduces energy consumption.
Focused quantization reduces CNN models by 18x while maintaining accuracy.
problem Memory and compute resource constraints in deploying CNNs on constrained devices.
method Focused quantization, exploiting weight distributions after pruning, dynamically discovers optimal numerical representations.
result Achieved a 18.08x compression ratio with only 0.24% loss in accuracy in ResNet-50.
Differentiable, Bit-shifting, and Scalable Quantization without training neural network from scratchcs.CV
This paper introduces a differentiable, scalable quantization method for neural networks.
problem Previous quantization methods lacked differentiability and scalability.
method The approach is differentiable and scalable, using bit-shifting and logarithmic quantization.
result The method achieves comparable accuracy to state-of-the-art approaches with less training time and lower inference cost.
Paper proposes a log-domain training method to reduce neural network complexity.
problem High computational complexity in training deep neural networks limits real-time training.
method End-to-end training and inference scheme using approximate logarithmic operations in the log-domain.
result 16-bit log-based training achieves within 1% accuracy of floating-point baselines.
Efficiently detects anomalies in videos with reduced computation.
problem Anomaly detection in videos for robust and efficient systems.
method Powers-of-two weights and denoising for CNN simplification and robustness.
result 10% faster detection with comparable accuracy and robustness.