SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity

no code implementations • 26 Jan 2025 • Zichen Fan, Steve Dai, Rangharajan Venkatesan, Dennis Sylvester, Brucek Khailany

Diffusion models have gained significant popularity in image generation tasks.

Image Generation Quantization

ConSmax: Hardware-Friendly Alternative Softmax with Learnable Parameters

1 code implementation • 31 Jan 2024 • Shiwei Liu, Guanchen Tao, Yifei Zou, Derek Chow, Zichen Fan, Kauna Lei, Bangfei Pan, Dennis Sylvester, Gregory Kielian, Mehdi Saligane

Experimental results show that ConSmax achieves a minuscule power consumption of 0. 2mW and an area of 0. 0008mm^2 at 1250MHz working frequency in 16nm FinFET technology.

Language Modelling Large Language Model

Efficient Computation Sharing for Multi-Task Visual Scene Understanding

1 code implementation • ICCV 2023 • Sara Shoouri, Mingyu Yang, Zichen Fan, Hun-Seok Kim

Solving multiple visual tasks using individual models can be resource-intensive, while multi-task learning can conserve resources by sharing knowledge across different tasks.

Multi-Task Learning Scene Understanding

RED: A ReRAM-based Deconvolution Accelerator

no code implementations • 5 Jul 2019 • Zichen Fan, Ziru Li, Bing Li, Yiran Chen, Hai, Li

Deconvolution has been widespread in neural networks.

Semantic Segmentation

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.