Analog/Mixed-Signal Circuit Synthesis Enabled by the Advancements of Circuit Architectures and Machine Learning Algorithms

no code implementations • 15 Dec 2021 • Shiyu Su, Qiaochu Zhang, Mohsen Hassanpourghadi, Juzheng Liu, Rezwan A Rasul, Mike Shuo-Wei Chen

Analog mixed-signal (AMS) circuit architecture has evolved towards more digital friendly due to technology scaling and demand for higher flexibility/reconfigurability.

BIG-bench Machine Learning

TAFA: Design Automation of Analog Mixed-Signal FIR Filters Using Time Approximation Architecture

no code implementations • 15 Dec 2021 • Shiyu Su, Qiaochu Zhang, Juzheng Liu, Mohsen Hassanpourghadi, Rezwan Rasul, Mike Shuo-Wei Chen

A digital finite impulse response (FIR) filter design is fully synthesizable, thanks to the mature CAD support of digital circuitry.

Quantization

Towards Efficient and Secure Delivery of Data for Deep Learning with Privacy-Preserving

1 code implementation • 17 Sep 2019 • Juncheng Shen, Juzheng Liu, Yiran Chen, Hai Li

When using MoLe for VGG-16 network on CIFAR dataset, the computational overhead is only 9% and the data transmission overhead is 5. 12%.

Privacy Preserving

Low-Power Computer Vision: Status, Challenges, Opportunities

no code implementations • 15 Apr 2019 • Sergei Alyamkin, Matthew Ardi, Alexander C. Berg, Achille Brighton, Bo Chen, Yiran Chen, Hsin-Pai Cheng, Zichen Fan, Chen Feng, Bo Fu, Kent Gauen, Abhinav Goel, Alexander Goncharenko, Xuyang Guo, Soonhoi Ha, Andrew Howard, Xiao Hu, Yuanjun Huang, Donghyun Kang, Jaeyoun Kim, Jong Gook Ko, Alexander Kondratyev, Junhyeok Lee, Seungjae Lee, Suwoong Lee, Zichao Li, Zhiyu Liang, Juzheng Liu, Xin Liu, Yang Lu, Yung-Hsiang Lu, Deeptanshu Malik, Hong Hanh Nguyen, Eunbyung Park, Denis Repin, Liang Shen, Tao Sheng, Fei Sun, David Svitov, George K. Thiruvathukal, Baiwu Zhang, Jingchi Zhang, Xiaopeng Zhang, Shaojie Zhuo

In addition to mobile phones, many autonomous systems rely on visual data for making decisions and some of these systems have limited energy (such as unmanned aerial vehicles also called drones and mobile robots).

2018 Low-Power Image Recognition Challenge

no code implementations • 3 Oct 2018 • Sergei Alyamkin, Matthew Ardi, Achille Brighton, Alexander C. Berg, Yiran Chen, Hsin-Pai Cheng, Bo Chen, Zichen Fan, Chen Feng, Bo Fu, Kent Gauen, Jongkook Go, Alexander Goncharenko, Xuyang Guo, Hong Hanh Nguyen, Andrew Howard, Yuanjun Huang, Donghyun Kang, Jaeyoun Kim, Alexander Kondratyev, Seungjae Lee, Suwoong Lee, Junhyeok Lee, Zhiyu Liang, Xin Liu, Juzheng Liu, Zichao Li, Yang Lu, Yung-Hsiang Lu, Deeptanshu Malik, Eunbyung Park, Denis Repin, Tao Sheng, Liang Shen, Fei Sun, David Svitov, George K. Thiruvathukal, Baiwu Zhang, Jingchi Zhang, Xiaopeng Zhang, Shaojie Zhuo

The Low-Power Image Recognition Challenge (LPIRC, https://rebootingcomputing. ieee. org/lpirc) is an annual competition started in 2015.

Towards Efficient and Secure Delivery of Data for Training and Inference with Privacy-Preserving

1 code implementation • 20 Sep 2018 • Juncheng Shen, Juzheng Liu, Yiran Chen, Hai Li

When using MoLe for VGG-16 network on CIFAR dataset, the computational overhead is only 9% and the data transmission overhead is 5. 12%.

Privacy Preserving