Gu-Yeon Wei

A Scalable Bayesian Inference Accelerator for Unsupervised Learning

Rob A. Rutenbar, Paul N. Whatmough, Yuji Chai, 2020, 2020 IEEE Hot Chips 32 Symposium (HCS).

SODA: a New Synthesis Infrastructure for Agile Hardware Design of Machine Learning Accelerators

Joseph Manzano, Marco Minutoli, Vito Giovanni Castellana, 2020, 2020 IEEE/ACM International Conference On Computer Aided Design (ICCAD).

Cheetah: Optimizing and Accelerating Homomorphic Encryption for Private Inference

Hsien-Hsin S. Lee, Brandon Reagen, Wooseok Choi, 2020, 2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA).

EdgeBERT: Optimizing On-Chip Inference for Multi-Task NLP pdf

Alexander M. Rush, Lillian Pentecost, Victor Sanh, 2020, ArXiv.

Cross-Stack Workload Characterization of Deep Recommendation Systems

Carole-Jean Wu, Gu-Yeon Wei, David Brooks, 2020, 2020 IEEE International Symposium on Workload Characterization (IISWC).

A binary-activation, multi-level weight RNN and training algorithm for ADC-/DAC-free and noise-resilient processing-in-memory inference with eNVM.

Gu-Yeon Wei, David Brooks, Siming Ma, 2020 .

A 3mm2 Programmable Bayesian Inference Accelerator for Unsupervised Machine Perception using Parallel Gibbs Sampling in 16nm

Rob A. Rutenbar, Paul N. Whatmough, Glenn G. Ko, 2020, 2020 IEEE Symposium on VLSI Circuits.

Invited: Software Defined Accelerators From Learning Tools Environment

Joseph Manzano, Marco Minutoli, Vito Giovanni Castellana, 2020, 2020 57th ACM/IEEE Design Automation Conference (DAC).