Deep learning is having a transformative effect on how sensor data are processed and interpreted. As a result, it is becoming increasingly feasible to build sensor-based computational models that are much more robust to real-world noise and complexity than previously possible. It is paramount that these innovations reach mobile and embedded devices that often rely on understanding and reacting to sensor data. However, deep models conventionally demand a level of system resources (e.g., memory and computation) that makes them problematic to run directly on constrained devices. In this work, we present the DeepX toolkit (DXTK); an opensource collection of software components for simplifying the execution of deep models on resource-sensitive platforms. DXTK contains a number of pre-trained low-resource deep models that users can quickly adopt and integrate for their particular application needs. It also offers a range of runtime options for executing deep models on range of devices including both Android and Linux variants. But the heart of DXTK is a series of optimization techniques (viz. weight/sparse factorization, convolution separation, precision scaling, and parameter cleaning). Each technique offers a complementary approach to shaping system resource requirements, and is compatible with deep and convolutional neural networks. We hope that DXTK proves to be a valuable resource for the community, and accelerates the adoption and study of resource-constrained deep learning.
As for transfer learning, Reference REF used the DeepX toolkit (DXTK), which includes several pre-trained low-resource deep learning models, to integrate models in mobile devices.
DXTK: Enabling Resource-efficient Deep Learning on Mobile and Embedded Devices with the DeepX Toolkit
MobiCASE
Computer Science