Intelligent Automation, Inc. — National Aeronautics and Space Administration SBIR Phase I: Z2

Description

In this proposed effort, we propose to develop a Deep Learning Processing Subsystem (DLPS) solution for HPSC system. The DLPS solution can significantly improve the performance and energy efficiency of HPSC system in processing deep learning algorithm. The key innovation of this proposal includes design and development of an low power and high performance deep learning processing system which include: (1) low-power and high performance DLPS hardware ; (2) HPSC-compatible software module to manage DLPS hardware and provide API to application layer; (3) DLPS toolchain to transform deep learning models from popular frameworks such as Keras, TensorFlow, and Caffe; (4) DLPS hardware implementation on space grade Xilinx FPGA platform for fault-tolerance design. Finally, all the proposed techniques will be integrated in a functional prototype to demonstrate the feasibility of proposed architecture.