RRAM-Based In-Memory Computing for Embedded Deep Neural Networks. - Daniel Bankman; J. Messner; Albert Gural; Boris Murmann (ACSSC, 2019)

Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Applications. - Albert Gural; Boris Murmann (ICML, 2019)

Trained Uniform Quantization for Accurate and Efficient Neural Network Inference on Fixed-Point Hardware. - Sambhav R. Jain; Albert Gural; Michael Wu 001; Chris Dick (CoRR, 2019)

Trained Quantization Thresholds for Accurate and Efficient Fixed-Point Inference of Deep Neural Networks. - Sambhav R. Jain; Albert Gural; Michael Wu 001; Chris Dick (MLSys, 2020)

Low-Rank Training of Deep Neural Networks for Emerging Memory Technology. - Albert Gural; Phillip M. Nadeau; Mehul Tikekar; Boris Murmann (CoRR, 2020)

CRADLE: Combined RF/Acoustic Detection and Localization of Passive Tags. - Angad S. Rekhi; Ernest So; Albert Gural; Amin Arbabian (IEEE Trans. Circuits Syst. I Regul. Pap., 2021)

CHIMERA: A 0.92-TOPS, 2.2-TOPS/W Edge AI Accelerator With 2-MByte On-Chip Foundry Resistive RAM for Efficient Training and Inference. - Kartik Prabhu; Albert Gural; Zainab F. Khan; Robert M. Radway; Massimo Giordano; Kalhan Koul; Rohan Doshi; John W. Kustin; Timothy Liu; Gregorio B. Lopes; Victor Turbiner; Win-San Khwa; et al. (IEEE J. Solid State Circuits, 2022)