Session

· Contributors · Organizations · Search Program · My Agenda

Research Manuscript: When They Go High - We Go Low (in Computation)

Event TypeResearch Manuscript

Virtual Programs

Keywords

Topics

Time

Location

DescriptionLow computational complexity improves hardware efficiency. To that end, this session reports cutting-edge developments in the domain of Approximate Computing for AI Hardware-Software codesign. Along with novel algorithmic techniques addressing pruning and reduced precision, the session dives deeper into cross-layer optimization and approximate arithmetic for different types of deep neural network accelerators ranging from embedded FPGA systems to in-memory computing.

Presentations

	BayesFT: Bayesian Optimization for Fault Tolerant Neural Network Architecture Authors Nanyang Ye Jingbiao Mei Zhicheng Fang Yuwen Zhang Ziqing Zhang Huaying Wu Xiaoyao Liang	Hosted in Virtual Platform
	A Unified DNN Weight Pruning Framework Using Reweighted Optimization Methods Authors Tianyun Zhang Xiaolong Ma Zheng Zhan Shanglin Zhou Caiwen Ding Makan Fardad Yanzhi Wang	Hosted in Virtual Platform
	CLAppED: A Design Framework for Implementing Cross-Layer Approximation in FPGA-based Embedded Systems Authors Salim Ullah Siva Satyendra Sahoo Akash Kumar	Hosted in Virtual Platform