Search Results for author:
Found 3 papers, 0 papers with code
A2Q+: Improving Accumulator-Aware Weight Quantization
Recent studies show that also reducing the precision of the accumulator can further improve hardware efficiency at the risk of numerical overflow, which introduces arithmetic errors that can degrade model accuracy.
A2Q: Accumulator-Aware Quantization with Guaranteed Overflow Avoidance
We apply our method to deep learning-based computer vision tasks to show that A2Q can train QNNs for low-precision accumulators while maintaining model accuracy competitive with a floating-point baseline.
Quantized Neural Networks for Low-Precision Accumulation with Guaranteed Overflow Avoidance
Across all of our benchmark models trained with 8-bit weights and activations, we observe that constraining the hidden layers of quantized neural networks to fit into 16-bit accumulators yields an average 98. 2% sparsity with an estimated compression rate of 46. 5x all while maintaining 99. 2% of the floating-point performance.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
