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[paper] ML based Aging-Aware FPGA Framework
Behnam Ghavami, Milad Ibrahimipour, Zhenman Fang, Lesley Shannon
MAPLE: A Machine Learning based Aging-Aware FPGA Architecture Exploration Framework
31st International Conference on Field-Programmable Logic and Applications
(FPL 2021 Short Paper),
Virtual Conference, Sept 2021
*Simon Fraser University, Burnaby, BC, CanadaAbstract: In this paper, we develop a framework called MAPLE to enable the aging-aware FPGA architecture exploration. The core idea is to efficiently model the aging-induced delay degradation at the coarse-grained FPGA basic block level using deep neural networks (DNNs). For each type of the FPGA basic block such as LUT and DSP, we first characterize its accurate delay degradation via transistor-level SPICE simulation under a versatile set of aging factors from the FPGA fabric and in-field operation. Then we train one DNN model for each block type to quickly and accurately predict the complex relation between its delay degradation and comprehensive aging factors. Moreover, we integrate our DNN models into the widely used Verilog-to-Routing toolflow (VTR 8) to support analyzing the impact of aging-induced delay degradation on the entire large scale FPGA architecture. Experimental results demonstrate that MAPLE can predict the delay degradation of FPGA blocks 104 to 107 times faster than transistor-level SPICE simulation, with a prediction error less than 0.7%. Our case study demonstrates that FPGA architects can effectively leverage MAPLE to explore better aging-aware FPGA architectures.
Fig: Overview of FPGA fabric and in-field factors affecting FPGA aging at transistor and basic block levels. We use DNNs to model FPGA delay degradation at basic block level.
Acknowledgements: We acknowledge the support from Government of Canada Technology Demonstration Program and MDA Systems Ltd; NSERC Discovery Grant RGPIN-2019-04613 and DGECR 2019-00120; Canada Foundation for Innovation John R. Evans Leaders Fund; Simon Fraser University New Faculty Start-up Grant; Xilinx, Huawei and Nvidia.
SK hynix Starts Mass Production of #1anm DRAM
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From Garage to Tech Giant
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