Quantization is a technique that can reduce the model size and also speed up the inference through low-precision computation.

We demonstrate that our quantization workflow achieves 6.9x inference throughput speedup on the ImageNet benchmark without sacrificing the model accuracy and ...

Quantization is a technique that can reduce the model size and also speed up the inference through low-precision computation.

We demonstrate that our quantization workflow achieves 6.9x inference throughput speedup on the ImageNet benchmark without sacrificing the model accuracy and ...

We presented a low-precision optimization framework for Bayesian deep learning that enables post-training quantization of BNNs using simple and familiar APIs.

Bayesian-Torch is the first framework to support low-precision quantization of BDL models for efficient inference and is widely used by research and developer ...

Abstract. We present a Post-Training Quantization (PTQ) flow for Bayesian Neural Networks (BNNs) to reduce the memory and compute requirements.

Dec 3, 2024 · Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable Bayesian inference in deep learning models.

Optimizing cpu performance for recommendation systems at-scale · Quantization for Bayesian Deep Learning: Low-Precision Characterization and Robustness · End-to- ...

Quantization for Bayesian Deep Learning: Low-Precision Characterization and Robustness ... In this chapter, we first show how incorporating Bayesian Deep Learning ...