ReLeQ : A Reinforcement Learning Approach for Automatic Deep Quantization of Neural Networks - Ahmed T. Elthakeb; Prannoy Pilligundla; Fatemehsadat Mireshghallah; Amir Yazdanbakhsh; Hadi Esmaeilzadeh (IEEE Micro, 2020)

Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation - Byung Hoon Ahn; Prannoy Pilligundla; Amir Yazdanbakhsh; Hadi Esmaeilzadeh (8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020, 2020)

Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks - Ahmed Taha Elthakeb; Prannoy Pilligundla; Fatemeh Mireshghallah; Alexander Cloninger; Hadi Esmaeilzadeh (Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, 2020)

Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation - Byung Hoon Ahn; Prannoy Pilligundla; Amir Yazdanbakhsh; Hadi Esmaeilzadeh (CoRR, 2020)

Gradient-Based Deep Quantization of Neural Networks through Sinusoidal Adaptive Regularization - Ahmed T. Elthakeb; Prannoy Pilligundla; Fatemehsadat Mireshghallah; Tarek Elgindi; Charles-Alban Deledalle; Hadi Esmaeilzadeh (CoRR, 2020)

SinReQ: Generalized Sinusoidal Regularization for Automatic Low-Bitwidth Deep Quantized Training - Ahmed T. Elthakeb; Prannoy Pilligundla; Hadi Esmaeilzadeh (CoRR, 2019)

Reinforcement Learning and Adaptive Sampling for Optimized DNN Compilation - Byung Hoon Ahn; Prannoy Pilligundla; Hadi Esmaeilzadeh (CoRR, 2019)

Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks - Ahmed T. Elthakeb; Prannoy Pilligundla; Hadi Esmaeilzadeh (CoRR, 2019)

ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks - Ahmed T. Elthakeb; Prannoy Pilligundla; Amir Yazdanbakhsh; Sean Kinzer; Hadi Esmaeilzadeh (CoRR, 2018)