2 papers
cs.CV2019
Trained Quantization Thresholds for Accurate and Efficient Fixed-Point Inference of Deep Neural Networks
Sambhav R. Jain, Albert Gural, Michael Wu +1
We propose a method of training quantization thresholds (TQT) for uniform symmetric quantizers using standard backpropagation and gradient descent. Contrary to prior work, we show…
cs.LG2018
Quantizing Convolutional Neural Networks for Low-Power High-Throughput Inference Engines
Sean O. Settle, Manasa Bollavaram, Paolo D'Alberto +6
Deep learning as a means to inferencing has proliferated thanks to its versatility and ability to approach or exceed human-level accuracy. These computational models have seemingly…