Machine Learning Accelerators

Selected Publications

Thierry Tambe, En-Yu Yang, Glenn G. Ko, Yuji Chai, Coleman Hooper, Marco Donato, Paul N. Whatmough, Alexander M. Rush, David Brooks and Gu-Yeon Wei
‘‘A 25mm2 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’’
IEEE International Conference on Solid-State Circuits (ISSCC)
San Francisco CA, USA, August 2020.

Glenn G. Ko, Yuji Chai, Marco Donato, Paul N. Whatmough, Thierry Tambe, Rob A. Rutenbar, David Brooks and Gu-Yeon Wei
‘‘A Scalable Bayesian Inference Accelerators for Unsupervised Learning’’
Hot Chips 32 Symposium (HCS)
Stanford University CA, USA, August 2020.

Glenn G. Ko, Yuji Chai, Marco Donato, Paul N. Whatmough, Thierry Tambe, Rob A. Rutenbar, David Brooks and Gu-Yeon Wei
‘‘A 3mm2 Programmable Bayesian Inference Accelerator for Unsupervised Machine Perception using Parallel Gibbs Sampling in 16nm’’
IEEE Symposium on VLSI Circuits (VLSI)
Honolulu, HI, USA, June 2020.

Glenn G. Ko, Yuji Chai, Rob A. Rutenbar, David Brooks and Gu-Yeon Wei
‘‘Accelerating Bayesian Inference for Structured Graphs Using Parallel Gibbs Sampling’’
International Conference on Field Programmable Logic & Applications (FPL)
Barcelona, Spain, September 2019.

P. N. Whatmough, S. K. Lee, M. Donato, H.-C. Hsueh, S. L. Xi, U. Gupta, L. Pentecost, G. G. Ko, D. Brooks and G.-Y. Wei
‘‘A 16nm 25mm2 SoC with a 54.5x Flexibility-Efficiency Range from Dual-Core Arm Cortex-A53, to eFPGA, and CacheCoherent Accelerators’’
IEEE Symposium on VLSI Circuits (VLSI)
Kyoto, Japan, June 2019.

Glenn G. Ko, Yuji Chai, Rob A. Rutenbar, David Brooks and Gu-Yeon Wei
‘‘FlexGibbs: Reconfigurable Parallel Gibbs Sampling Accelerator for Structured Graphs’’
IEEE International Symposium on Field-Programmable Custom Computing Machine (FCCM)
San Diego, CA, USA, April 2019.

Yu Wang, Yuhao Zhu, Glenn G. Ko, Brandon Reagen, Gu-Yeon Wei and David Brooks
‘‘Demystifying Bayesian Inference Workloads’’
IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)
Madison, WI, USA, March 2019.

Glenn G. Ko and Rob A. Rutenbar
‘‘Real-Time and Low-Power Streaming Source Separation using Markov Random Field’’
ACM Journal on Emerging Technologies in Computer Systems (JETC)
April 2019.

Glenn G. Ko and Rob A. Rutenbar
‘‘A Case Study on Machine Learning Hardware: Real-time Source Separation using Markov Random Fields via Sampling-based Inference.’’
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
New Orleans, LA, USA, March 2017.

Chuanjun Zhang, Glenn G. Ko, Jung Wook Choi, Shang-nien Tsai, Minje Kim, Abner Guzman-Rivera, Rob Rutenbar, Paris Smargdis, Mi Sun Park, Vijay Narayanan, Hongyi Xin, Onur Mutlu, Bin Li, Li Zhao, Mei Chen, and Ravi Iyer
‘‘EMERALD: Characterization of Emerging Applications and Algorithms for Low-power Devices.’’
IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS),
Austin, TX, USA, April 2013.

Minje Kim, Paris Smaragdis, Glenn G. Ko, and Rob A. Rutenbar
‘‘Stereophonic Spectrogram Segmentation Using Markov Random Fields.’’
IEEE International Workshop on Machine Learning for Signal Processing (MLSP),
Santander, Spain, September 2012.