Glenn G. Ko

Publication Activity (10 Years)

Years Active: 2012-2023
Publications (10 Years): 17

Top Topics

Bayesian Inference

Top Venues

IEEE J. Solid State Circuits

Publications

Thierry Tambe, En-Yu Yang, Glenn G. Ko, Yuji Chai, Coleman Hooper, Marco Donato, Paul N. Whatmough, Alexander M. Rush, David Brooks, Gu-Yeon Wei
A 16-nm SoC for Noise-Robust Speech and NLP Edge AI Inference With Bayesian Sound Source Separation and Attention-Based DNNs. IEEE J. Solid State Circuits 58 (2) (2023)
Yuji Chai, John Gkountouras, Glenn G. Ko, David Brooks, Gu-Yeon Wei
INT2.1: Towards Fine-Tunable Quantized Large Language Models with Error Correction through Low-Rank Adaptation. CoRR (2023)
Yuji Chai, Luke Bailey, Yunho Jin, Matthew Karle, Glenn G. Ko
Bigger&Faster: Two-stage Neural Architecture Search for Quantized Transformer Models. CoRR (2022)
Sae Kyu Lee, Paul N. Whatmough, Marco Donato, Glenn G. Ko, David Brooks, Gu-Yeon Wei
SMIV: A 16-nm 25-mm² SoC for IoT With Arm Cortex-A53, eFPGA, and Coherent Accelerators. IEEE J. Solid State Circuits 57 (2) (2022)
Yuji Chai, Glenn G. Ko, Wei-Te Mark Ting, Luke Bailey, David Brooks, Gu-Yeon Wei
CoopMC: Algorithm-Architecture Co-Optimization for Markov Chain Monte Carlo Accelerators. HPCA (2022)
Thierry Tambe, En-Yu Yang, Glenn G. Ko, Yuji Chai, Coleman Hooper, Marco Donato, Paul N. Whatmough, Alexander M. Rush, David Brooks, Gu-Yeon Wei
SoC for IoT Devices with 18ms Noise-Robust Speech-to-Text Latency via Bayesian Speech Denoising and Attention-Based Sequence-to-Sequence DNN Speech Recognition in 16nm FinFET. ISSCC (2021)
Thierry Tambe, En-Yu Yang, Glenn G. Ko, Yuji Chai, Coleman Hooper, Marco Donato, Paul N. Whatmough, Alexander M. Rush, David Brooks, Gu-Yeon Wei
Hot Chips Symposium - August 22-24, 2021. HCS (2021)
Glenn G. Ko, Yuji Chai, Marco Donato, Paul N. Whatmough, Thierry Tambe, Rob A. Rutenbar, Gu-Yeon Wei, David Brooks
A Scalable Bayesian Inference Accelerator for Unsupervised Learning. Hot Chips Symposium (2020)
Glenn G. Ko, Yuji Chai, Marco Donato, Paul N. Whatmough, Thierry Tambe, Rob A. Rutenbar, David Brooks, Gu-Yeon Wei
Programmable Bayesian Inference Accelerator for Unsupervised Machine Perception using Parallel Gibbs Sampling in 16nm. VLSI Circuits (2020)
Paul N. Whatmough, Marco Donato, Glenn G. Ko, David Brooks, Gu-Yeon Wei
CHIPKIT: An agile, reusable open-source framework for rapid test chip development. CoRR (2020)
Paul N. Whatmough, Marco Donato, Glenn G. Ko, Sae Kyu Lee, David Brooks, Gu-Yeon Wei
CHIPKIT: An Agile, Reusable Open-Source Framework for Rapid Test Chip Development. IEEE Micro 40 (4) (2020)
Glenn G. Ko, Yuji Chai, Rob A. Rutenbar, David Brooks, Gu-Yeon Wei
Accelerating Bayesian Inference on Structured Graphs Using Parallel Gibbs Sampling. FPL (2019)
Paul N. Whatmough, Sae Kyu Lee, Marco Donato, Hsea-Ching Hsueh, Sam Likun Xi, Udit Gupta, Lillian Pentecost, Glenn G. Ko, David M. Brooks, Gu-Yeon Wei
SoC with a 54.5x Flexibility-Efficiency Range from Dual-Core Arm Cortex-A53 to eFPGA and Cache-Coherent Accelerators. VLSI Circuits (2019)
Glenn G. Ko, Yuji Chai, Rob A. Rutenbar, David Brooks, Gu-Yeon Wei
FlexGibbs: Reconfigurable Parallel Gibbs Sampling Accelerator for Structured Graphs. FCCM (2019)
Yu Emma Wang, Yuhao Zhu, Glenn G. Ko, Brandon Reagen, Gu-Yeon Wei, David Brooks
Demystifying Bayesian Inference Workloads. ISPASS (2019)
Glenn G. Ko, Rob A. Rutenbar
Real-Time and Low-Power Streaming Source Separation Using Markov Random Field. ACM J. Emerg. Technol. Comput. Syst. 14 (2) (2018)
Glenn G. Ko, Rob A. Rutenbar
A case study of machine learning hardware: Real-time source separation using Markov Random Fields via sampling-based inference. ICASSP (2017)
Chuanjun Zhang, Glenn G. Ko, Jungwook Choi, Shang-nien Tsai, Minje Kim, Abner Guzmán-Rivera, Rob A. Rutenbar, Paris Smaragdis, Mi Sun Park, Vijaykrishnan Narayanan, Hongyi Xin, Onur Mutlu, Bin Li, Li Zhao, Mei Chen
EMERALD: Characterization of emerging applications and algorithms for low-power devices. ISPASS (2013)
Minje Kim, Paris Smaragdis, Glenn G. Ko, Rob A. Rutenbar
Stereophonic spectrogram segmentation using Markov random fields. MLSP (2012)