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Found 7 papers, 3 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.
Post-Training Quantization with Low-precision Minifloats and Integers on FPGAs
However, the exploration of floating-point formats smaller than 8 bits and their comparison with integer quantization remains relatively limited.
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.
QONNX: Representing Arbitrary-Precision Quantized Neural Networks
We present extensions to the Open Neural Network Exchange (ONNX) intermediate representation format to represent arbitrary-precision quantized neural networks.
Ps and Qs: Quantization-aware pruning for efficient low latency neural network inference
We study various configurations of pruning during quantization-aware training, which we term quantization-aware pruning, and the effect of techniques like regularization, batch normalization, and different pruning schemes on performance, computational complexity, and information content metrics.
FINN-L: Library Extensions and Design Trade-off Analysis for Variable Precision LSTM Networks on FPGAs
In this paper, we present the first systematic exploration of this design space as a function of precision for Bidirectional Long Short-Term Memory (BiLSTM) neural network.
Optical Character Recognition
Optical Character Recognition (OCR)
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