high performance computing on graphics processing units: hgpu.org

This work proposes TinyDL, an automated end-to-end framework that aims to integrate the state-of-the-art Deep Learning (DL) models into embedded systems. TinyDL enables efficient training and execution of DL models as data is collected over time while adhering to the underlying physical resources and constraints. The constraints can be characterized in terms of memory bandwidth, energy resources (e.g., battery life), and/or real-time requirement. TinyDL takes advantage of platform profiling to abstract the physical characteristics of the target embedded device. We introduce a platform-aware signal transformation methodology to enable DL training and execution within the confine of the available resources. Our approach balances the trade-off between data movements and computations to improve the performance of costly iterative DL training/execution. Proof-of-concept implementation on NVIDIA Jetson TK1 embedded platform demonstrates up to two orders of magnitude energy improvement over the previous DL solutions, none of which had been amenable to constrained devices.