55 citations · 75 across the 3 of their papers we have counts for
3 papers
StruM: Structured Mixed Precision for Efficient Deep Learning Hardware Codesign
Michael Wu, Arnab Raha, Deepak A. Mathaikutty +3
In this paper, we propose StruM, a novel structured mixed-precision-based deep learning inference method, co-designed with its associated hardware accelerator (DPU), to address the…
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…
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…