- DATE 2021 became a virtual conference due to the worldwide COVID-19 pandemic (click here for more details)
Taking into consideration the continued erratic development of the worldwide COVID-19 pandemic and the accompanying restrictions of worldwide travelling as well as the safety and health of the DATE community, the Organizing Committees decided to host DATE 2021 as a virtual conference in early February 2021. Unfortunately, the current situation does not allow a face-to-face conference in Grenoble, France.
The Organizing Committees are working intensively to create a virtual conference that gives as much of a real conference atmosphere as possible.
IP1_3 Interactive Presentations
Date: Tuesday, 02 February 2021
Time: 09:50 - 10:20
Interactive Presentations run simultaneously during a 30-minute slot. Additionally, each IP paper is briefly introduced in a one-minute presentation in a corresponding regular session
|IP1_3.1||FORSETI: AN EFFICIENT BASIC-BLOCK-LEVEL SENSITIVITY ANALYSIS FRAMEWORK TOWARDS MULTI-BIT FAULTS
Jinting Ren, Chongqing University, CN
Jinting Ren, Xianzhang Chen, Duo Liu, Moming Duan, Renping Liu and Chengliang Wang, Chongqing University, CN
The per-instruction sensitivity analysis framework is developed to evaluate the resiliency of a program and identify the segments of the program needing protection. However, for multi-bit hardware faults, the per-instruction sensitivity analysis frameworks can cause large overhead for redundant analyses. In this paper, we propose a basic-block-level sensitivity analysis framework, Forseti, to reduce the analysis overhead in analyzing impacts of modern microprocessors' multi-bit faults on programs. We implement Forseti in LLVM and evaluate it with five typical workloads. Extensive experimental results show that Forseti can achieve more than 90% sensitivity classification accuracy and 6.16x speedup over instruction-level analysis.
|IP1_3.2||MODELING SILICON-PHOTONIC NEURAL NETWORKS UNDER UNCERTAINTIES
Sanmitra Banerjee, Duke University, US
Sanmitra Banerjee1, Mahdi Nikdast2 and Krishnendu Chakrabarty1
1Duke University, US; 2Colorado State University, US
Silicon-photonic neural networks (SPNNs) offer substantial improvements in computing speed and energy efficiency compared to their digital electronic counterparts. However, the energy efficiency and accuracy of SPNNs are highly impacted by uncertainties that arise from fabrication-process and thermal variations. In this paper, we present the first comprehensive and hierarchical study on the impact of random uncertainties on the classification accuracy of a Mach--Zehnder Interferometer (MZI)-based SPNN. We show that such impact can vary based on both the location and characteristics (e.g., tuned phase angles) of a non-ideal silicon-photonic device. Simulation results show that in an SPNN with two hidden layers and 1374 tunable-thermal-phase shifters, random uncertainties even in mature fabrication processes can lead to a catastrophic 70% accuracy loss.